AutomationForexTrading.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutomationForexTrading.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAQtaHTxKedB",
        "outputId": "64f39c2c-bb7c-46bb-c361-9682c3638fce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Forex-Trading-Automation-with-Deep-Reinforcement-Learning'...\n",
            "remote: Enumerating objects: 45, done.\u001b[K\n",
            "remote: Counting objects: 100% (45/45), done.\u001b[K\n",
            "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
            "remote: Total 45 (delta 8), reused 38 (delta 6), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (45/45), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone -b master https://github.com/TomatoFT/Forex-Trading-Automation-with-Deep-Reinforcement-Learning"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev libgl1-mesa-glx\n",
        "%cd /content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning\n",
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mwKnGZ1VOBR7",
        "outputId": "db5dddfa-316c-44e5-c6f3-aded49c110c3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Reading package lists... Done\n",
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            "libopenmpi-dev is already the newest version (2.1.1-8).\n",
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            "python3-dev set to manually installed.\n",
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            "zlib1g-dev set to manually installed.\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
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            "0 upgraded, 1 newly installed, 0 to remove and 67 not upgraded.\n",
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            "/content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning\n",
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            "\u001b[?25hRequirement already satisfied: atari_py~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from gym==0.15.3->-r requirements.txt (line 6)) (0.2.9)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from gym==0.15.3->-r requirements.txt (line 6)) (7.1.2)\n",
            "Building wheels for collected packages: gym, gast, mpi4py\n",
            "  Building wheel for gym (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gym: filename=gym-0.15.3-py3-none-any.whl size=1644970 sha256=bb67ea2434ddf6f2ee76d9ab2277f97a3b33098803fa770fd938a5fb24bbc0af\n",
            "  Stored in directory: /root/.cache/pip/wheels/55/16/6b/2250ca4f9f050a4d27d8bed287e57bbb3c33fc4066f557cc75\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7554 sha256=74a2b654513a82ea373d00e82cbf35cfffafe05bba954070925f1d93dec5c757\n",
            "  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3\n",
            "  Building wheel for mpi4py (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mpi4py: filename=mpi4py-3.1.3-cp37-cp37m-linux_x86_64.whl size=2185270 sha256=e106f81b36a77ebc996fe4a11ef47ef27e5c2cecd81edf64c83e2fb7ad7cdc30\n",
            "  Stored in directory: /root/.cache/pip/wheels/7a/07/14/6a0c63fa2c6e473c6edc40985b7d89f05c61ff25ee7f0ad9ac\n",
            "Successfully built gym gast mpi4py\n",
            "Installing collected packages: numpy, pyglet, cloudpickle, gym, wheel, setuptools, pandas, matplotlib, joblib, tensorflow-estimator, tensorboard, stable-baselines, pluggy, mpi4py, keras-applications, gast, tensorflow, stockstats, scikit-learn, pytest\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.21.6\n",
            "    Uninstalling numpy-1.21.6:\n",
            "      Successfully uninstalled numpy-1.21.6\n",
            "  Attempting uninstall: pyglet\n",
            "    Found existing installation: pyglet 1.5.0\n",
            "    Uninstalling pyglet-1.5.0:\n",
            "      Successfully uninstalled pyglet-1.5.0\n",
            "  Attempting uninstall: cloudpickle\n",
            "    Found existing installation: cloudpickle 1.3.0\n",
            "    Uninstalling cloudpickle-1.3.0:\n",
            "      Successfully uninstalled cloudpickle-1.3.0\n",
            "  Attempting uninstall: gym\n",
            "    Found existing installation: gym 0.17.3\n",
            "    Uninstalling gym-0.17.3:\n",
            "      Successfully uninstalled gym-0.17.3\n",
            "  Attempting uninstall: wheel\n",
            "    Found existing installation: wheel 0.37.1\n",
            "    Uninstalling wheel-0.37.1:\n",
            "      Successfully uninstalled wheel-0.37.1\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 57.4.0\n",
            "    Uninstalling setuptools-57.4.0:\n",
            "      Successfully uninstalled setuptools-57.4.0\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.3.5\n",
            "    Uninstalling pandas-1.3.5:\n",
            "      Successfully uninstalled pandas-1.3.5\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.1.0\n",
            "    Uninstalling joblib-1.1.0:\n",
            "      Successfully uninstalled joblib-1.1.0\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.8.0\n",
            "    Uninstalling tensorflow-estimator-2.8.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.8.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.8.0\n",
            "    Uninstalling tensorboard-2.8.0:\n",
            "      Successfully uninstalled tensorboard-2.8.0\n",
            "  Attempting uninstall: pluggy\n",
            "    Found existing installation: pluggy 0.7.1\n",
            "    Uninstalling pluggy-0.7.1:\n",
            "      Successfully uninstalled pluggy-0.7.1\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.5.3\n",
            "    Uninstalling gast-0.5.3:\n",
            "      Successfully uninstalled gast-0.5.3\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.8.2+zzzcolab20220527125636\n",
            "    Uninstalling tensorflow-2.8.2+zzzcolab20220527125636:\n",
            "      Successfully uninstalled tensorflow-2.8.2+zzzcolab20220527125636\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "  Attempting uninstall: pytest\n",
            "    Found existing installation: pytest 3.6.4\n",
            "    Uninstalling pytest-3.6.4:\n",
            "      Successfully uninstalled pytest-3.6.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.21.0 which is incompatible.\n",
            "xarray 0.20.2 requires numpy>=1.18, but you have numpy 1.16.4 which is incompatible.\n",
            "xarray 0.20.2 requires pandas>=1.1, but you have pandas 1.0.3 which is incompatible.\n",
            "xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.16.4 which is incompatible.\n",
            "tensorflow-probability 0.16.0 requires cloudpickle>=1.3, but you have cloudpickle 1.2.2 which is incompatible.\n",
            "tensorflow-probability 0.16.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\n",
            "tables 3.7.0 requires numpy>=1.19.0, but you have numpy 1.16.4 which is incompatible.\n",
            "scikit-image 0.18.3 requires numpy>=1.16.5, but you have numpy 1.16.4 which is incompatible.\n",
            "pywavelets 1.3.0 requires numpy>=1.17.3, but you have numpy 1.16.4 which is incompatible.\n",
            "pyerfa 2.0.0.1 requires numpy>=1.17, but you have numpy 1.16.4 which is incompatible.\n",
            "pyarrow 6.0.1 requires numpy>=1.16.6, but you have numpy 1.16.4 which is incompatible.\n",
            "kapre 0.3.7 requires numpy>=1.18.5, but you have numpy 1.16.4 which is incompatible.\n",
            "kapre 0.3.7 requires tensorflow>=2.0.0, but you have tensorflow 1.15.4 which is incompatible.\n",
            "jaxlib 0.3.7+cuda11.cudnn805 requires numpy>=1.19, but you have numpy 1.16.4 which is incompatible.\n",
            "jax 0.3.8 requires numpy>=1.19, but you have numpy 1.16.4 which is incompatible.\n",
            "imbalanced-learn 0.8.1 requires scikit-learn>=0.24, but you have scikit-learn 0.21.0 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas>=1.1.0; python_version >= \"3.0\", but you have pandas 1.0.3 which is incompatible.\n",
            "fbprophet 0.7.1 requires pandas>=1.0.4, but you have pandas 1.0.3 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "astropy 4.3.1 requires numpy>=1.17, but you have numpy 1.16.4 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed cloudpickle-1.2.2 gast-0.2.2 gym-0.15.3 joblib-0.15.1 keras-applications-1.0.8 matplotlib-3.2.1 mpi4py-3.1.3 numpy-1.16.4 pandas-1.0.3 pluggy-0.13.1 pyglet-1.3.2 pytest-5.4.3 scikit-learn-0.21.0 setuptools-41.6.0 stable-baselines-2.10.2 stockstats-0.4.1 tensorboard-1.15.0 tensorflow-1.15.4 tensorflow-estimator-1.15.1 wheel-0.33.6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits",
                  "numpy",
                  "pkg_resources"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning\n",
        "!python run_DRL.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGPAJ7NmORKV",
        "outputId": "f6132559-0af7-4261-fd09-40c7fe56b6d1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning\n",
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines/__init__.py:33: UserWarning: stable-baselines is in maintenance mode, please use [Stable-Baselines3 (SB3)](https://github.com/DLR-RM/stable-baselines3) for an up-to-date version. You can find a [migration guide](https://stable-baselines3.readthedocs.io/en/master/guide/migration.html) in SB3 documentation.\n",
            "  \"stable-baselines is in maintenance mode, please use [Stable-Baselines3 (SB3)](https://github.com/DLR-RM/stable-baselines3) for an up-to-date version. You can find a [migration guide](https://stable-baselines3.readthedocs.io/en/master/guide/migration.html) in SB3 documentation.\"\n",
            "Enter Forex Environment\n",
            "Enter Forex Environment\n",
            "Enter Forex Trading Environment\n",
            "   datadate  ... turbulence\n",
            "0  20180102  ...        0.0\n",
            "1  20180102  ...        0.0\n",
            "2  20180102  ...        0.0\n",
            "3  20180102  ...        0.0\n",
            "4  20180102  ...        0.0\n",
            "\n",
            "[5 rows x 12 columns]\n",
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            "============Start Ensemble Strategy============\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.939075, 0.65449, 74.42699999999999, 1.0635649999999999, 0.692415, 79.2555, 113.72, 1.6231200000000001, 1.5244950000000002, 1.062455, 0.9014549999999999, 120.8175, 10.71275, 1.72615, 10.4713, 1.123945, 1.8009400000000002, 1.6912200000000002, 1.17861, 134.035, 1.91495, 1.24682, 69.993, 0.651105, 1.3564, 0.945305, 107.4965, 22.48046, 9.53095, 9.3165, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00501468767616875, 0.0024731132697204394, 0.545485884880307, -0.0004488902136161599, 0.0054059368456123025, 0.16755622203420728, 0.4172830656000599, -0.007529514820488803, 0.001439978670421338, -0.0006364046788103206, 0.0038686727006203014, 0.36954961162912525, -0.0200236522011199, -0.008718600747381622, -0.021330116801616228, 0.003970241094275462, -0.01631692523003858, -0.005748159267419561, -0.005833487090428857, -0.1639532661226042, -0.018198407181463594, -0.0009474353397420643, 0.5428593657004654, 0.005352914226691263, -0.0036829613818001317, -0.004000949101071072, -0.0500245383796738, 0.018668158840799975, -0.05435115547521896, -0.053862346081453794, 60.621261362890046, 55.70082395944016, 57.91835710070404, 49.60652566136237, 59.22171433812733, 52.81222572739099, 57.026892815961624, 42.866859607156044, 50.31028970764129, 48.22432801485542, 54.39376745406849, 54.40068769096825, 46.33407367909293, 42.67321160560536, 45.088351703247355, 55.377464217153346, 36.37912281152824, 45.830383813133885, 44.86717901736138, 49.96956365395756, 36.5483013077948, 50.300575467205626, 58.18749242635541, 59.60211997990984, 45.50836862630947, 42.822360423672926, 49.44284647186307, 48.63656249985095, 44.47824012828285, 42.976158771357895, 0.9374121715522742, 0.6536096137043812, 74.04711204523798, 1.0678327685616698, 0.6895096046333847, 78.98902542637857, 113.28734824235518, 1.6303728805840456, 1.5284305188122127, 1.0656795644242048, 0.9038569902177616, 120.72720197078483, 10.78236730477574, 1.7410096316123775, 10.48651549006644, 1.1241626432656326, 1.8038760869999828, 1.691022582301391, 1.179061834086832, 133.57084502576242, 1.9261624366920025, 1.243741057404675, 69.34715644285804, 0.6457394787771852, 1.35962388296142, 0.947984600396671, 107.39251981512417, 22.68557636027212, 9.591635093733865, 9.328339982500161, 0.9362252500000002, 0.6557302499999998, 74.171325, 1.06807725, 0.6905887499999996, 79.22325, 113.11055000000006, 1.6311737499999992, 1.527179250000001, 1.0696087500000004, 0.9000334999999996, 120.98635000000009, 10.7424055, 1.742255249999999, 10.488172750000004, 1.1264635000000003, 1.8123675000000001, 1.6968135000000004, 1.1884682500000001, 134.42772499999992, 1.935730749999999, 1.251615749999999, 69.44430000000008, 0.6465850000000005, 1.3557532500000005, 0.9495365000000003, 107.40082500000014, 22.43534125000003, 9.537128000000004, 9.310880499999998]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20200702\n",
            "======TD3 Training========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/tf_util.py:191: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/tf_util.py:200: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/td3.py:131: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/policies.py:124: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/layers/core.py:332: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/tf_layers.py:57: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.Dense instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/td3.py:164: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/td3.py:194: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/td3.py:209: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/td3.py:226: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/td3.py:237: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/td3/td3.py:240: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
            "\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 4         |\n",
            "| fps                     | 126       |\n",
            "| mean 100 episode reward | -5.6      |\n",
            "| n_updates               | 2600      |\n",
            "| qf1_loss                | 103646.64 |\n",
            "| qf2_loss                | 79058.78  |\n",
            "| time_elapsed            | 22        |\n",
            "| total timesteps         | 2780      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 8         |\n",
            "| fps                     | 119       |\n",
            "| mean 100 episode reward | -5.2      |\n",
            "| n_updates               | 5400      |\n",
            "| qf1_loss                | 177284.34 |\n",
            "| qf2_loss                | 106552.86 |\n",
            "| time_elapsed            | 46        |\n",
            "| total timesteps         | 5560      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 117       |\n",
            "| mean 100 episode reward | -5        |\n",
            "| n_updates               | 8200      |\n",
            "| qf1_loss                | 139555.66 |\n",
            "| qf2_loss                | 92987.88  |\n",
            "| time_elapsed            | 71        |\n",
            "| total timesteps         | 8340      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 16        |\n",
            "| fps                     | 112       |\n",
            "| mean 100 episode reward | -5        |\n",
            "| n_updates               | 11000     |\n",
            "| qf1_loss                | 35193.414 |\n",
            "| qf2_loss                | 38484.92  |\n",
            "| time_elapsed            | 98        |\n",
            "| total timesteps         | 11120     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 20        |\n",
            "| fps                     | 112       |\n",
            "| mean 100 episode reward | -5        |\n",
            "| n_updates               | 13800     |\n",
            "| qf1_loss                | 37424.395 |\n",
            "| qf2_loss                | 15590.634 |\n",
            "| time_elapsed            | 123       |\n",
            "| total timesteps         | 13900     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 24        |\n",
            "| fps                     | 112       |\n",
            "| mean 100 episode reward | -4.9      |\n",
            "| n_updates               | 16500     |\n",
            "| qf1_loss                | 34297.98  |\n",
            "| qf2_loss                | 27689.887 |\n",
            "| time_elapsed            | 148       |\n",
            "| total timesteps         | 16680     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 28        |\n",
            "| fps                     | 111       |\n",
            "| mean 100 episode reward | -4.9      |\n",
            "| n_updates               | 19300     |\n",
            "| qf1_loss                | 13431.513 |\n",
            "| qf2_loss                | 113432.15 |\n",
            "| time_elapsed            | 174       |\n",
            "| total timesteps         | 19460     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  3.010889196395874  minutes\n",
            "======TD3 Validation from:  20200702 to  20200910\n",
            "[0.040677865312004124, 0.9574299999999999, 0.66459, 77.3335, 1.089685, 0.7283149999999999, 80.768, 116.37, 1.620435, 1.55165, 1.076915, 0.90775, 125.3245, 10.644715, 1.7659, 10.323139999999999, 1.1803, 1.7853, 1.7093049999999999, 1.18635, 138.0545, 1.9452, 1.300225, 70.97, 0.66842, 1.31459, 0.912415, 106.1785, 21.36505, 9.019355000000001, 8.746585000000001, 7.0, 3800.0, 0, 1900.0, 3800.0, 1900.0, 2472.0, 0, 0, 0.0, 0, 0.0, 2008.0, 1938.0, 0.0, 1900.0, 0, 0.0, 0, 1900.0, 1900.0, 0, 0, 0, 0, 0, 1868.0, 1801.0, 1900.0, 1900.0, 0.0014248575832682686, 0.002791102365437337, 0.42746144955822274, 0.0014239777908919837, 0.004231976844812357, 0.32896546846440344, 0.15767273124441772, -0.004856931671838316, -0.0023197529126486316, 0.0013328625154671592, -0.0006628211974187836, 0.3257851705679684, -0.00404813929050718, -0.0029644053164270545, 0.0066049592858643535, 0.003396158141445005, -0.004085938697180991, -0.0012981107632754618, 0.002370929232829111, 0.462556043483886, -0.0019044843632294306, 0.004734101632439813, 0.301339584102152, 0.003035344686450947, -0.005857789127087676, -0.0015791628618881948, -0.03272933271199463, -0.19340298717630944, -0.030875619008476463, -0.02101953624202224, 55.620526070563706, 55.62734677968166, 58.64864814959615, 55.75034756142569, 59.07480650178056, 54.170708540419895, 54.357462653175006, 44.11597250782523, 49.97834601482987, 51.888654208700835, 55.02206663383619, 56.037714818468665, 50.03618578525474, 48.65011607973483, 46.75527719369358, 57.4006809293384, 40.21754017382815, 45.662926693552954, 46.67078950692218, 50.72021297782478, 44.10622498936248, 51.62072747311859, 55.306109297451755, 56.047579555824, 43.152363510542244, 43.83163351198604, 48.51398934203697, 41.15276718398912, 46.32524502671467, 42.25698960955608, 0.9553104943003609, 0.6634680239031147, 77.183456468886, 1.0891172214788116, 0.7273715027883153, 80.79592208512565, 116.3358401039916, 1.6265717781394469, 1.5538015834787597, 1.0790687877816698, 0.8986052832161396, 125.5418444455167, 10.582867495570728, 1.771577271998238, 10.34414938647266, 1.1830493185665396, 1.8100422810493657, 1.7291211829678566, 1.2008714603368937, 139.7052546837364, 1.9713061830712626, 1.3165648970060153, 70.87074576962594, 0.6678737125828754, 1.3134180758612424, 0.9121350777427478, 106.11597843330318, 21.699685872807443, 8.945544527450778, 8.743351420836047, 0.9531260000000004, 0.6594052500000002, 76.88635000000006, 1.0927065000000007, 0.7247187499999996, 80.66782499999998, 116.60185000000006, 1.634693999999999, 1.5579880000000017, 1.077836750000001, 0.8984867500000002, 125.68012500000006, 10.5496925, 1.7863287500000002, 10.330397500000007, 1.1845990000000002, 1.8193122500000012, 1.7339867500000004, 1.1996385000000005, 139.87784999999994, 1.9879817499999994, 1.3184554999999996, 70.36747500000013, 0.6632642500000003, 1.3152110000000004, 0.9098842499999998, 106.09422500000012, 21.88922625000003, 8.906041250000005, 8.720496749999999]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  0.16925650910992476\n",
            "======PPO Training========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/tf_layers.py:123: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/ppo2/ppo2.py:198: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
            "\n",
            "Training time (PPO):  1.290991989771525  minutes\n",
            "======PPO Validation from:  20200702 to  20200910\n",
            "[340960.13289613026, 0.9574299999999999, 0.66459, 77.3335, 1.089685, 0.7283149999999999, 80.768, 116.37, 1.620435, 1.55165, 1.076915, 0.90775, 125.3245, 10.644715, 1.7659, 10.323139999999999, 1.1803, 1.7853, 1.7093049999999999, 1.18635, 138.0545, 1.9452, 1.300225, 70.97, 0.66842, 1.31459, 0.912415, 106.1785, 21.36505, 9.019355000000001, 8.746585000000001, 2492.040123939514, 0.0, 1334.7417674064636, 979.699028968811, 576.030918598175, 100.0, 1713.2596008777618, 49.52098399400711, 222.3759230673313, 0.0, 469.07814598083496, 1285.936861038208, 1084.0076469182968, 100.0, 0.0, 34.43454170227051, 373.0522708892822, 407.83401679992676, 1655.1781367063522, 660.648494720459, 0.0, 699.9017658829689, 0.0, 2660.756447315216, 1191.3358144760132, 0.0, 331.44146156311035, 1819.5412447452545, 55.263078689575195, 0.0, 0.0014248575832682686, 0.002791102365437337, 0.42746144955822274, 0.0014239777908919837, 0.004231976844812357, 0.32896546846440344, 0.15767273124441772, -0.004856931671838316, -0.0023197529126486316, 0.0013328625154671592, -0.0006628211974187836, 0.3257851705679684, -0.00404813929050718, -0.0029644053164270545, 0.0066049592858643535, 0.003396158141445005, -0.004085938697180991, -0.0012981107632754618, 0.002370929232829111, 0.462556043483886, -0.0019044843632294306, 0.004734101632439813, 0.301339584102152, 0.003035344686450947, -0.005857789127087676, -0.0015791628618881948, -0.03272933271199463, -0.19340298717630944, -0.030875619008476463, -0.02101953624202224, 55.620526070563706, 55.62734677968166, 58.64864814959615, 55.75034756142569, 59.07480650178056, 54.170708540419895, 54.357462653175006, 44.11597250782523, 49.97834601482987, 51.888654208700835, 55.02206663383619, 56.037714818468665, 50.03618578525474, 48.65011607973483, 46.75527719369358, 57.4006809293384, 40.21754017382815, 45.662926693552954, 46.67078950692218, 50.72021297782478, 44.10622498936248, 51.62072747311859, 55.306109297451755, 56.047579555824, 43.152363510542244, 43.83163351198604, 48.51398934203697, 41.15276718398912, 46.32524502671467, 42.25698960955608, 0.9553104943003609, 0.6634680239031147, 77.183456468886, 1.0891172214788116, 0.7273715027883153, 80.79592208512565, 116.3358401039916, 1.6265717781394469, 1.5538015834787597, 1.0790687877816698, 0.8986052832161396, 125.5418444455167, 10.582867495570728, 1.771577271998238, 10.34414938647266, 1.1830493185665396, 1.8100422810493657, 1.7291211829678566, 1.2008714603368937, 139.7052546837364, 1.9713061830712626, 1.3165648970060153, 70.87074576962594, 0.6678737125828754, 1.3134180758612424, 0.9121350777427478, 106.11597843330318, 21.699685872807443, 8.945544527450778, 8.743351420836047, 0.9531260000000004, 0.6594052500000002, 76.88635000000006, 1.0927065000000007, 0.7247187499999996, 80.66782499999998, 116.60185000000006, 1.634693999999999, 1.5579880000000017, 1.077836750000001, 0.8984867500000002, 125.68012500000006, 10.5496925, 1.7863287500000002, 10.330397500000007, 1.1845990000000002, 1.8193122500000012, 1.7339867500000004, 1.1996385000000005, 139.87784999999994, 1.9879817499999994, 1.3184554999999996, 70.36747500000013, 0.6632642500000003, 1.3152110000000004, 0.9098842499999998, 106.09422500000012, 21.88922625000003, 8.906041250000005, 8.720496749999999]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  0.0731851298052841\n",
            "======DDPG Training========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/ddpg/ddpg.py:444: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/common/tf_util.py:432: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "Training time (DDPG):  2.2166993379592896  minutes\n",
            "======DDPG Validation from:  20200702 to  20200910\n",
            "[0.08775972382957442, 0.9574299999999999, 0.66459, 77.3335, 1.089685, 0.7283149999999999, 80.768, 116.37, 1.620435, 1.55165, 1.076915, 0.90775, 125.3245, 10.644715, 1.7659, 10.323139999999999, 1.1803, 1.7853, 1.7093049999999999, 1.18635, 138.0545, 1.9452, 1.300225, 70.97, 0.66842, 1.31459, 0.912415, 106.1785, 21.36505, 9.019355000000001, 8.746585000000001, 3200.0, 3136.076026916504, 0, 3204.0, 3099.99991607666, 0, 0, 3200.0, 0, 3199.9999771118164, 3200.999954223633, 3199.9870681762695, 0, 3198.1967163085938, 3254.0, 3199.979476928711, 3219.9999389648438, 1454.4416573047638, 0, 0, 0, 2222.8016090393066, 2199.6392364501953, 3201.0, 3020.72225189209, 3200.0, 2882.0417098999023, 0, 3099.924217224121, 3197.755905151367, 0.0014248575832682686, 0.002791102365437337, 0.42746144955822274, 0.0014239777908919837, 0.004231976844812357, 0.32896546846440344, 0.15767273124441772, -0.004856931671838316, -0.0023197529126486316, 0.0013328625154671592, -0.0006628211974187836, 0.3257851705679684, -0.00404813929050718, -0.0029644053164270545, 0.0066049592858643535, 0.003396158141445005, -0.004085938697180991, -0.0012981107632754618, 0.002370929232829111, 0.462556043483886, -0.0019044843632294306, 0.004734101632439813, 0.301339584102152, 0.003035344686450947, -0.005857789127087676, -0.0015791628618881948, -0.03272933271199463, -0.19340298717630944, -0.030875619008476463, -0.02101953624202224, 55.620526070563706, 55.62734677968166, 58.64864814959615, 55.75034756142569, 59.07480650178056, 54.170708540419895, 54.357462653175006, 44.11597250782523, 49.97834601482987, 51.888654208700835, 55.02206663383619, 56.037714818468665, 50.03618578525474, 48.65011607973483, 46.75527719369358, 57.4006809293384, 40.21754017382815, 45.662926693552954, 46.67078950692218, 50.72021297782478, 44.10622498936248, 51.62072747311859, 55.306109297451755, 56.047579555824, 43.152363510542244, 43.83163351198604, 48.51398934203697, 41.15276718398912, 46.32524502671467, 42.25698960955608, 0.9553104943003609, 0.6634680239031147, 77.183456468886, 1.0891172214788116, 0.7273715027883153, 80.79592208512565, 116.3358401039916, 1.6265717781394469, 1.5538015834787597, 1.0790687877816698, 0.8986052832161396, 125.5418444455167, 10.582867495570728, 1.771577271998238, 10.34414938647266, 1.1830493185665396, 1.8100422810493657, 1.7291211829678566, 1.2008714603368937, 139.7052546837364, 1.9713061830712626, 1.3165648970060153, 70.87074576962594, 0.6678737125828754, 1.3134180758612424, 0.9121350777427478, 106.11597843330318, 21.699685872807443, 8.945544527450778, 8.743351420836047, 0.9531260000000004, 0.6594052500000002, 76.88635000000006, 1.0927065000000007, 0.7247187499999996, 80.66782499999998, 116.60185000000006, 1.634693999999999, 1.5579880000000017, 1.077836750000001, 0.8984867500000002, 125.68012500000006, 10.5496925, 1.7863287500000002, 10.330397500000007, 1.1845990000000002, 1.8193122500000012, 1.7339867500000004, 1.1996385000000005, 139.87784999999994, 1.9879817499999994, 1.3184554999999996, 70.36747500000013, 0.6632642500000003, 1.3152110000000004, 0.9098842499999998, 106.09422500000012, 21.88922625000003, 8.906041250000005, 8.720496749999999]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/kfac.py:99: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/kfac.py:298: The name tf.diag is deprecated. Please use tf.linalg.tensor_diag instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/kfac.py:548: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/acktr.py:307: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/kfac.py:973: The name tf.train.MomentumOptimizer is deprecated. Please use tf.compat.v1.train.MomentumOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/kfac.py:914: The name tf.mod is deprecated. Please use tf.math.mod instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/kfac.py:621: The name tf.self_adjoint_eig is deprecated. Please use tf.linalg.eigh instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/stable_baselines/acktr/acktr.py:319: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "--------------------------------------\n",
            "| explained_variance      | -0.402   |\n",
            "| fps                     | 17       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | -1.91    |\n",
            "| reference_Q_mean        | 1.01     |\n",
            "| reference_Q_std         | 0.547    |\n",
            "| reference_action_mean   | 0.244    |\n",
            "| reference_action_std    | 0.948    |\n",
            "| reference_actor_Q_mean  | 1.12     |\n",
            "| reference_actor_Q_std   | 0.607    |\n",
            "| rollout/Q_mean          | 0.474    |\n",
            "| rollout/actions_mean    | 0.0675   |\n",
            "| rollout/actions_std     | 0.813    |\n",
            "| rollout/episode_steps   | 695      |\n",
            "| rollout/episodes        | 28       |\n",
            "| rollout/return          | -7.5     |\n",
            "| rollout/return_history  | -7.5     |\n",
            "| total/duration          | 132      |\n",
            "| total/episodes          | 28       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 151      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -1.05    |\n",
            "| train/loss_critic       | 0.1      |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.0139   |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.136   |\n",
            "| fps                | 235      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 42.2     |\n",
            "| policy_loss        | -0.482   |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 0.355    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.00348 |\n",
            "| fps                | 244      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 42.2     |\n",
            "| policy_loss        | 0.118    |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 0.0799   |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0338  |\n",
            "| fps                | 246      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 42.3     |\n",
            "| policy_loss        | -0.631   |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 0.125    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0264   |\n",
            "| fps                | 245      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 42.3     |\n",
            "| policy_loss        | -0.297   |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.283    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0111   |\n",
            "| fps                | 246      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 42.3     |\n",
            "| policy_loss        | 0.421    |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 1.38     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0729  |\n",
            "| fps                | 247      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 42.3     |\n",
            "| policy_loss        | 0.548    |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 4.58     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0176   |\n",
            "| fps                | 248      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 42.3     |\n",
            "| policy_loss        | 1.71     |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 3.16     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.648    |\n",
            "| fps                | 249      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 42.4     |\n",
            "| policy_loss        | -0.997   |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 0.222    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.00294  |\n",
            "| fps                | 249      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 42.5     |\n",
            "| policy_loss        | -0.618   |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 6.8      |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.196    |\n",
            "| fps                | 249      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 42.5     |\n",
            "| policy_loss        | 1.01     |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 0.669    |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.3925119638442993  minutes\n",
            "======ACKTR Validation from:  20200702 to  20200910\n",
            "[20137.34432285488, 0.9574299999999999, 0.66459, 77.3335, 1.089685, 0.7283149999999999, 80.768, 116.37, 1.620435, 1.55165, 1.076915, 0.90775, 125.3245, 10.644715, 1.7659, 10.323139999999999, 1.1803, 1.7853, 1.7093049999999999, 1.18635, 138.0545, 1.9452, 1.300225, 70.97, 0.66842, 1.31459, 0.912415, 106.1785, 21.36505, 9.019355000000001, 8.746585000000001, 2541.9925298690796, 0.0, 0.0, 0.0, 88.8290023803711, 0.0, 1408.066658258438, 357.698757648468, 3854.812533378601, 0.0, 0.0, 320.86879110336304, 0, 130.5279245376587, 146.90263175964355, 3208.762293815613, 1578.4626421928406, 2270.0567220449448, 0.0, 4891.0195236206055, 0.0, 783.6612286567688, 1029.9694375991821, 0, 689.9406182765961, 0.0, 0.0, 200.0, 0.0, 163.39022064208984, 0.0014248575832682686, 0.002791102365437337, 0.42746144955822274, 0.0014239777908919837, 0.004231976844812357, 0.32896546846440344, 0.15767273124441772, -0.004856931671838316, -0.0023197529126486316, 0.0013328625154671592, -0.0006628211974187836, 0.3257851705679684, -0.00404813929050718, -0.0029644053164270545, 0.0066049592858643535, 0.003396158141445005, -0.004085938697180991, -0.0012981107632754618, 0.002370929232829111, 0.462556043483886, -0.0019044843632294306, 0.004734101632439813, 0.301339584102152, 0.003035344686450947, -0.005857789127087676, -0.0015791628618881948, -0.03272933271199463, -0.19340298717630944, -0.030875619008476463, -0.02101953624202224, 55.620526070563706, 55.62734677968166, 58.64864814959615, 55.75034756142569, 59.07480650178056, 54.170708540419895, 54.357462653175006, 44.11597250782523, 49.97834601482987, 51.888654208700835, 55.02206663383619, 56.037714818468665, 50.03618578525474, 48.65011607973483, 46.75527719369358, 57.4006809293384, 40.21754017382815, 45.662926693552954, 46.67078950692218, 50.72021297782478, 44.10622498936248, 51.62072747311859, 55.306109297451755, 56.047579555824, 43.152363510542244, 43.83163351198604, 48.51398934203697, 41.15276718398912, 46.32524502671467, 42.25698960955608, 0.9553104943003609, 0.6634680239031147, 77.183456468886, 1.0891172214788116, 0.7273715027883153, 80.79592208512565, 116.3358401039916, 1.6265717781394469, 1.5538015834787597, 1.0790687877816698, 0.8986052832161396, 125.5418444455167, 10.582867495570728, 1.771577271998238, 10.34414938647266, 1.1830493185665396, 1.8100422810493657, 1.7291211829678566, 1.2008714603368937, 139.7052546837364, 1.9713061830712626, 1.3165648970060153, 70.87074576962594, 0.6678737125828754, 1.3134180758612424, 0.9121350777427478, 106.11597843330318, 21.699685872807443, 8.945544527450778, 8.743351420836047, 0.9531260000000004, 0.6594052500000002, 76.88635000000006, 1.0927065000000007, 0.7247187499999996, 80.66782499999998, 116.60185000000006, 1.634693999999999, 1.5579880000000017, 1.077836750000001, 0.8984867500000002, 125.68012500000006, 10.5496925, 1.7863287500000002, 10.330397500000007, 1.1845990000000002, 1.8193122500000012, 1.7339867500000004, 1.1996385000000005, 139.87784999999994, 1.9879817499999994, 1.3184554999999996, 70.36747500000013, 0.6632642500000003, 1.3152110000000004, 0.9098842499999998, 106.09422500000012, 21.88922625000003, 8.906041250000005, 8.720496749999999]\n",
            "adjcp:  30\n",
            "======Trading from:  20200910 to  20201117\n",
            "previous_total_asset:1000000\n",
            "end_total_asset:997224.0817484946\n",
            "total_reward:-2775.9182515053544\n",
            "total_cost:  1467.1592325049987\n",
            "total trades:  784\n",
            "Sharpe:  -0.03924624738514393\n",
            "previous_total_asset:1000000\n",
            "end_total_asset:994405.5988700989\n",
            "total_reward:-5594.40112990106\n",
            "total_cost:  1023.0821319925427\n",
            "total trades:  979\n",
            "Sharpe:  -0.09157234538095103\n",
            "previous_total_asset:1000000\n",
            "end_total_asset:997237.4942911936\n",
            "total_reward:-2762.5057088064495\n",
            "total_cost:  1933.2014843302409\n",
            "total trades:  1258\n",
            "Sharpe:  -0.08480725025304721\n",
            "previous_total_asset:1000000\n",
            "end_total_asset:1008899.8436933795\n",
            "total_reward:8899.843693379546\n",
            "total_cost:  1530.3830306863638\n",
            "total trades:  866\n",
            "Sharpe:  0.1335059331779614\n",
            "previous_total_asset:1000000\n",
            "end_total_asset:997224.0817484946\n",
            "total_reward:-2775.9182515053544\n",
            "total_cost:  1467.1592325049987\n",
            "total trades:  784\n",
            "Sharpe:  -0.03924624738514393\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.957475, 0.660945, 77.0295, 1.0912899999999999, 0.72582, 80.45100000000001, 116.545, 1.62792, 1.558685, 1.0759450000000002, 0.92265, 125.391, 10.724065, 1.7763200000000001, 10.368730000000001, 1.1815149999999999, 1.764345, 1.686525, 1.166145, 135.907, 1.925405, 1.28059, 70.5865, 0.6651, 1.319165, 0.91061, 106.126, 21.4435, 9.0761, 8.776225, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.001507310532557038, 0.002425345383669786, 0.3870444310585839, 0.0014756604432741849, 0.003824072887951835, 0.27949409930482716, 0.16059979657396184, -0.004469494498826609, -0.0018096510842378066, 0.0010006668216526737, 0.001323517071413205, 0.2892649662898208, 0.00794604140742905, -0.0024801158415670432, 0.008279130012434521, 0.0030123853187644656, -0.007555874534420193, -0.0046933572059999484, -0.0005826042500765016, 0.12190911300544371, -0.005593782888572596, 0.0014747552994736068, 0.2606207911863123, 0.0026260263243653714, -0.004997960471549101, -0.0015630103512607185, -0.028939921091080123, -0.20245950904900312, -0.017740588278613245, -0.016601605437234213, 55.64176672377074, 53.190945092346894, 56.929737946129215, 56.68067396364408, 57.63109420507398, 52.69693239767275, 54.9872728638037, 46.62753187525895, 52.04371412722148, 51.150173021472774, 61.47147694281149, 56.27846667622629, 52.41593591707078, 50.924941748952826, 49.62259187412687, 57.85845657309562, 36.35252428023338, 40.93759286666794, 40.64576874155075, 44.97715638915919, 40.51624706646354, 46.23566293923687, 53.25357372574646, 54.1681791431281, 44.93050545994002, 42.985434786953824, 48.24037594521565, 42.14301871789795, 47.915267151952705, 43.71426621199476, 0.9557040407912042, 0.6630092922843664, 77.155464383634, 1.0895122721190276, 0.727089411372258, 80.73320897873917, 116.37386917599312, 1.62681690938682, 1.5546894773917124, 1.0785008263668208, 0.9029770499041143, 125.51441818269547, 10.608539769103324, 1.7724395861803766, 10.348618588932176, 1.1827703515544417, 1.8017336844949357, 1.7213764224282462, 1.1945575584574584, 139.01466292305705, 1.9629605134219419, 1.3100240066412852, 70.81906472060304, 0.6673694012041709, 1.3144629711591982, 0.9118577908804302, 106.11780053633896, 21.65310662320609, 8.969281886096091, 8.749328435229494, 0.9537310000000003, 0.6599337500000002, 76.91572500000007, 1.0926787500000008, 0.7252657499999996, 80.64734999999999, 116.55225000000007, 1.6334679999999993, 1.5578140000000016, 1.077898750000001, 0.8994190000000002, 125.63370000000006, 10.561207, 1.78493225, 10.335589500000006, 1.1846085000000002, 1.816136250000001, 1.7319130000000005, 1.1985050000000004, 139.68684999999996, 1.9844619999999993, 1.3171494999999998, 70.39622500000011, 0.6637820000000004, 1.3150505000000003, 0.9099272499999997, 106.05410000000013, 21.852933750000027, 8.915704750000005, 8.724842999999998]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20200910\n",
            "======TD3 Training========\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 4         |\n",
            "| fps                     | 124       |\n",
            "| mean 100 episode reward | -3.2      |\n",
            "| n_updates               | 2800      |\n",
            "| qf1_loss                | 176969.9  |\n",
            "| qf2_loss                | 228354.94 |\n",
            "| time_elapsed            | 23        |\n",
            "| total timesteps         | 2980      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 8         |\n",
            "| fps                     | 123       |\n",
            "| mean 100 episode reward | -3.5      |\n",
            "| n_updates               | 5800      |\n",
            "| qf1_loss                | 147983.2  |\n",
            "| qf2_loss                | 134799.06 |\n",
            "| time_elapsed            | 48        |\n",
            "| total timesteps         | 5960      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 121       |\n",
            "| mean 100 episode reward | -3.7      |\n",
            "| n_updates               | 8800      |\n",
            "| qf1_loss                | 128268.87 |\n",
            "| qf2_loss                | 127886.31 |\n",
            "| time_elapsed            | 73        |\n",
            "| total timesteps         | 8940      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 16        |\n",
            "| fps                     | 121       |\n",
            "| mean 100 episode reward | -3.8      |\n",
            "| n_updates               | 11800     |\n",
            "| qf1_loss                | 194197.05 |\n",
            "| qf2_loss                | 192685.22 |\n",
            "| time_elapsed            | 98        |\n",
            "| total timesteps         | 11920     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 20        |\n",
            "| fps                     | 121       |\n",
            "| mean 100 episode reward | -3.9      |\n",
            "| n_updates               | 14800     |\n",
            "| qf1_loss                | 101745.67 |\n",
            "| qf2_loss                | 112064.33 |\n",
            "| time_elapsed            | 123       |\n",
            "| total timesteps         | 14900     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 24        |\n",
            "| fps                     | 120       |\n",
            "| mean 100 episode reward | -3.9      |\n",
            "| n_updates               | 17700     |\n",
            "| qf1_loss                | 198531.38 |\n",
            "| qf2_loss                | 244425.1  |\n",
            "| time_elapsed            | 148       |\n",
            "| total timesteps         | 17880     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  2.789279599984487  minutes\n",
            "======TD3 Validation from:  20200910 to  20201117\n",
            "[0.09006283418101269, 0.957175, 0.668025, 76.54899999999999, 1.060345, 0.732105, 79.98, 114.57799999999999, 1.618795, 1.54958, 1.08159, 0.89775, 123.9415, 10.7346, 1.7166099999999997, 10.2164, 1.185175, 1.802615, 1.7254349999999998, 1.20487, 137.9985, 1.9114, 1.32019, 72.199, 0.6904399999999999, 1.3074649999999999, 0.912535, 104.575, 20.29189, 9.060105, 8.623085000000001, 2100.0, 4200.0, 4200.0, 4200.0, 4200.0, 0, 2398.0, 0, 0, 3048.0, 2106.0, 2100.0, 2100.0, 2100.0, 4200.0, 0, 4200.0, 0, 0, 0, 2100.0, 0, 0, 5.0, 0, 0, 0.0, 2100.0, 0, 0, 0.0027740135060171367, 0.003695470364546316, 0.3414499612963766, -0.0037975081814072276, 0.004031075849914245, 0.12692208663757754, -0.1256260062791057, -0.006756815380105197, -0.0018330568394422375, 0.0016161275159318755, -0.0028513107289530604, 0.050801675177794436, -0.04396275944704975, -0.013357495801520791, -0.04793248891627755, 0.0017412592175956032, -0.001824386105538478, 0.0033354114322630135, 0.0055793221954760774, 0.48383536753337353, -0.00884339701416259, 0.006031476090923027, 0.5736456400487668, 0.006172158900414115, -0.00351058366549184, 1.2910336204097206e-05, -0.11119782368828623, -0.2625066647496439, -0.04674191819385598, -0.05323323494294563, 56.558651244179934, 57.63007331343629, 55.6176172458145, 42.08318106947576, 58.1002847513486, 51.40271597799311, 47.57773931072592, 44.1111126410454, 49.32490182611905, 57.59354869416344, 47.204660420850665, 51.30250451589714, 46.91928572493703, 39.66125799806949, 42.97167155803307, 54.54725448616931, 46.54019557341987, 51.88702981689826, 55.12202139627551, 53.31339458037436, 42.40094685983489, 55.624772754983574, 60.338313245611566, 63.148378099797405, 44.661938266959936, 48.86039298683612, 46.2576691889795, 39.05612090335749, 45.58027376561716, 41.804101630068345, 0.9499632647541526, 0.6614059906228291, 75.92570444129221, 1.0637479169572626, 0.7251822152497736, 79.92598348759616, 114.797436894087, 1.6289875982287716, 1.547403156958585, 1.0772529128598958, 0.8979240156970457, 123.6755723705694, 10.800291034351972, 1.733026624912506, 10.246594746874965, 1.1812204986176429, 1.814075643860278, 1.7232605455544905, 1.1998499219326693, 137.72766432165204, 1.929652473109375, 1.3155214520832406, 71.38072006259475, 0.6817416744133504, 1.3100116927814105, 0.9120126169692085, 104.7016914660845, 20.560518778741695, 9.154870504248546, 8.676311501134503, 0.9437705000000001, 0.65384575, 75.09012500000007, 1.0657262500000009, 0.7181029999999997, 79.56422499999998, 114.84804999999997, 1.6419229999999991, 1.5494455000000027, 1.0732922500000002, 0.9006339999999999, 123.27592500000006, 10.893041250000003, 1.7499434999999999, 10.298656250000008, 1.1789092499999994, 1.8230849999999996, 1.7204977499999998, 1.1918297500000006, 136.8840749999999, 1.9428619999999994, 1.3090529999999996, 70.46092500000016, 0.6738210000000002, 1.3143485000000008, 0.9104529999999995, 104.56730000000016, 20.82629400000003, 9.24522175000001, 8.736897749999997]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  -0.04060065276326876\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.2367215275764465  minutes\n",
            "======PPO Validation from:  20200910 to  20201117\n",
            "[596292.1287464752, 0.957175, 0.668025, 76.54899999999999, 1.060345, 0.732105, 79.98, 114.57799999999999, 1.618795, 1.54958, 1.08159, 0.89775, 123.9415, 10.7346, 1.7166099999999997, 10.2164, 1.185175, 1.802615, 1.7254349999999998, 1.20487, 137.9985, 1.9114, 1.32019, 72.199, 0.6904399999999999, 1.3074649999999999, 0.912535, 104.575, 20.29189, 9.060105, 8.623085000000001, 3092.490246772766, 306.36057698726654, 0.0, 42.23240661621094, 235.38998556137085, 153.91805505752563, 316.6900842189789, 0.0, 330.65156066417694, 80.61447596549988, 290.3682928085327, 594.1648549437523, 133.67725563049316, 115.96272087097168, 198.82592296600342, 0.0, 0.0, 404.5067992210388, 2113.8026304244995, 312.3416829109192, 515.895362585783, 148.06932258605957, 2533.2739963531494, 0.0, 0.0, 1504.469276189804, 74.16294479370117, 761.351958990097, 1300.825370311737, 1018.4817923307419, 0.0027740135060171367, 0.003695470364546316, 0.3414499612963766, -0.0037975081814072276, 0.004031075849914245, 0.12692208663757754, -0.1256260062791057, -0.006756815380105197, -0.0018330568394422375, 0.0016161275159318755, -0.0028513107289530604, 0.050801675177794436, -0.04396275944704975, -0.013357495801520791, -0.04793248891627755, 0.0017412592175956032, -0.001824386105538478, 0.0033354114322630135, 0.0055793221954760774, 0.48383536753337353, -0.00884339701416259, 0.006031476090923027, 0.5736456400487668, 0.006172158900414115, -0.00351058366549184, 1.2910336204097206e-05, -0.11119782368828623, -0.2625066647496439, -0.04674191819385598, -0.05323323494294563, 56.558651244179934, 57.63007331343629, 55.6176172458145, 42.08318106947576, 58.1002847513486, 51.40271597799311, 47.57773931072592, 44.1111126410454, 49.32490182611905, 57.59354869416344, 47.204660420850665, 51.30250451589714, 46.91928572493703, 39.66125799806949, 42.97167155803307, 54.54725448616931, 46.54019557341987, 51.88702981689826, 55.12202139627551, 53.31339458037436, 42.40094685983489, 55.624772754983574, 60.338313245611566, 63.148378099797405, 44.661938266959936, 48.86039298683612, 46.2576691889795, 39.05612090335749, 45.58027376561716, 41.804101630068345, 0.9499632647541526, 0.6614059906228291, 75.92570444129221, 1.0637479169572626, 0.7251822152497736, 79.92598348759616, 114.797436894087, 1.6289875982287716, 1.547403156958585, 1.0772529128598958, 0.8979240156970457, 123.6755723705694, 10.800291034351972, 1.733026624912506, 10.246594746874965, 1.1812204986176429, 1.814075643860278, 1.7232605455544905, 1.1998499219326693, 137.72766432165204, 1.929652473109375, 1.3155214520832406, 71.38072006259475, 0.6817416744133504, 1.3100116927814105, 0.9120126169692085, 104.7016914660845, 20.560518778741695, 9.154870504248546, 8.676311501134503, 0.9437705000000001, 0.65384575, 75.09012500000007, 1.0657262500000009, 0.7181029999999997, 79.56422499999998, 114.84804999999997, 1.6419229999999991, 1.5494455000000027, 1.0732922500000002, 0.9006339999999999, 123.27592500000006, 10.893041250000003, 1.7499434999999999, 10.298656250000008, 1.1789092499999994, 1.8230849999999996, 1.7204977499999998, 1.1918297500000006, 136.8840749999999, 1.9428619999999994, 1.3090529999999996, 70.46092500000016, 0.6738210000000002, 1.3143485000000008, 0.9104529999999995, 104.56730000000016, 20.82629400000003, 9.24522175000001, 8.736897749999997]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  0.07651716222373425\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.178161549568176  minutes\n",
            "======DDPG Validation from:  20200910 to  20201117\n",
            "[0.15803642224894077, 0.957175, 0.668025, 76.54899999999999, 1.060345, 0.732105, 79.98, 114.57799999999999, 1.618795, 1.54958, 1.08159, 0.89775, 123.9415, 10.7346, 1.7166099999999997, 10.2164, 1.185175, 1.802615, 1.7254349999999998, 1.20487, 137.9985, 1.9114, 1.32019, 72.199, 0.6904399999999999, 1.3074649999999999, 0.912535, 104.575, 20.29189, 9.060105, 8.623085000000001, 1900.0, 1900.0, 1799.9962539672852, 1900.0, 0, 0, 1799.9995880126953, 1835.999740600586, 0, 1799.8042678833008, 1799.4574508666992, 0, 1800.0, 1799.947135925293, 1799.9548416137695, 0, 1799.9995346069336, 0, 0, 1799.999984741211, 0, 0, 1900.0, 1900.0, 1799.9946594238281, 1799.9251098632812, 1899.999885559082, 0, 1799.9995040893555, 0, 0.0027740135060171367, 0.003695470364546316, 0.3414499612963766, -0.0037975081814072276, 0.004031075849914245, 0.12692208663757754, -0.1256260062791057, -0.006756815380105197, -0.0018330568394422375, 0.0016161275159318755, -0.0028513107289530604, 0.050801675177794436, -0.04396275944704975, -0.013357495801520791, -0.04793248891627755, 0.0017412592175956032, -0.001824386105538478, 0.0033354114322630135, 0.0055793221954760774, 0.48383536753337353, -0.00884339701416259, 0.006031476090923027, 0.5736456400487668, 0.006172158900414115, -0.00351058366549184, 1.2910336204097206e-05, -0.11119782368828623, -0.2625066647496439, -0.04674191819385598, -0.05323323494294563, 56.558651244179934, 57.63007331343629, 55.6176172458145, 42.08318106947576, 58.1002847513486, 51.40271597799311, 47.57773931072592, 44.1111126410454, 49.32490182611905, 57.59354869416344, 47.204660420850665, 51.30250451589714, 46.91928572493703, 39.66125799806949, 42.97167155803307, 54.54725448616931, 46.54019557341987, 51.88702981689826, 55.12202139627551, 53.31339458037436, 42.40094685983489, 55.624772754983574, 60.338313245611566, 63.148378099797405, 44.661938266959936, 48.86039298683612, 46.2576691889795, 39.05612090335749, 45.58027376561716, 41.804101630068345, 0.9499632647541526, 0.6614059906228291, 75.92570444129221, 1.0637479169572626, 0.7251822152497736, 79.92598348759616, 114.797436894087, 1.6289875982287716, 1.547403156958585, 1.0772529128598958, 0.8979240156970457, 123.6755723705694, 10.800291034351972, 1.733026624912506, 10.246594746874965, 1.1812204986176429, 1.814075643860278, 1.7232605455544905, 1.1998499219326693, 137.72766432165204, 1.929652473109375, 1.3155214520832406, 71.38072006259475, 0.6817416744133504, 1.3100116927814105, 0.9120126169692085, 104.7016914660845, 20.560518778741695, 9.154870504248546, 8.676311501134503, 0.9437705000000001, 0.65384575, 75.09012500000007, 1.0657262500000009, 0.7181029999999997, 79.56422499999998, 114.84804999999997, 1.6419229999999991, 1.5494455000000027, 1.0732922500000002, 0.9006339999999999, 123.27592500000006, 10.893041250000003, 1.7499434999999999, 10.298656250000008, 1.1789092499999994, 1.8230849999999996, 1.7204977499999998, 1.1918297500000006, 136.8840749999999, 1.9428619999999994, 1.3090529999999996, 70.46092500000016, 0.6738210000000002, 1.3143485000000008, 0.9104529999999995, 104.56730000000016, 20.82629400000003, 9.24522175000001, 8.736897749999997]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 5.96e-08 |\n",
            "| fps                     | 16       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | -0.615   |\n",
            "| reference_Q_mean        | 1.89     |\n",
            "| reference_Q_std         | 0.552    |\n",
            "| reference_action_mean   | 0.267    |\n",
            "| reference_action_std    | 0.964    |\n",
            "| reference_actor_Q_mean  | 2.41     |\n",
            "| reference_actor_Q_std   | 0.477    |\n",
            "| rollout/Q_mean          | 0.737    |\n",
            "| rollout/actions_mean    | 0.0438   |\n",
            "| rollout/actions_std     | 0.83     |\n",
            "| rollout/episode_steps   | 745      |\n",
            "| rollout/episodes        | 26       |\n",
            "| rollout/return          | -6.21    |\n",
            "| rollout/return_history  | -6.21    |\n",
            "| total/duration          | 130      |\n",
            "| total/episodes          | 26       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 154      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -2.32    |\n",
            "| train/loss_critic       | 0.118    |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.00742  |\n",
            "--------------------------------------\n",
            "----------------------------------\n",
            "| explained_variance | -2.38e-07 |\n",
            "| fps                | 236       |\n",
            "| nupdates           | 100       |\n",
            "| policy_entropy     | 42.9      |\n",
            "| policy_loss        | -1.87     |\n",
            "| total_timesteps    | 2000      |\n",
            "| value_loss         | 0.385     |\n",
            "----------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0        |\n",
            "| fps                | 246      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | -0.575   |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 1.78     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0229   |\n",
            "| fps                | 249      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 43       |\n",
            "| policy_loss        | -1.69    |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 2.91     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0584   |\n",
            "| fps                | 252      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 43       |\n",
            "| policy_loss        | 0.284    |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.16     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0907  |\n",
            "| fps                | 252      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -0.209   |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 0.188    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.695   |\n",
            "| fps                | 253      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -1.17    |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 2.77     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.18     |\n",
            "| fps                | 253      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -0.401   |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 0.5      |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.112    |\n",
            "| fps                | 253      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | -0.566   |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 0.136    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0478  |\n",
            "| fps                | 253      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | -0.153   |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 0.751    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 5.96e-08 |\n",
            "| fps                | 254      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | -0.279   |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 6.83     |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.3597917993863424  minutes\n",
            "======ACKTR Validation from:  20200910 to  20201117\n",
            "[11069.347145747928, 0.957175, 0.668025, 76.54899999999999, 1.060345, 0.732105, 79.98, 114.57799999999999, 1.618795, 1.54958, 1.08159, 0.89775, 123.9415, 10.7346, 1.7166099999999997, 10.2164, 1.185175, 1.802615, 1.7254349999999998, 1.20487, 137.9985, 1.9114, 1.32019, 72.199, 0.6904399999999999, 1.3074649999999999, 0.912535, 104.575, 20.29189, 9.060105, 8.623085000000001, 3412.944109559059, 42.69468307495117, 2839.934173107147, 843.4882900714874, 421.8257579803467, 2311.413956642151, 1813.0588281154633, 0.0, 0.0, 69.93817901611328, 4066.7145829200745, 0.0, 106.76113510131836, 1748.1837248802185, 2845.574644088745, 0.0, 3855.452734053135, 2601.345789909363, 1758.7352695465088, 1111.2435946464539, 101.33884251117706, 2492.7282435894012, 0.0, 0.0, 120.4556303024292, 0.0, 743.6565673351288, 2853.1368079185486, 76.04490661621094, 3256.354322910309, 0.0027740135060171367, 0.003695470364546316, 0.3414499612963766, -0.0037975081814072276, 0.004031075849914245, 0.12692208663757754, -0.1256260062791057, -0.006756815380105197, -0.0018330568394422375, 0.0016161275159318755, -0.0028513107289530604, 0.050801675177794436, -0.04396275944704975, -0.013357495801520791, -0.04793248891627755, 0.0017412592175956032, -0.001824386105538478, 0.0033354114322630135, 0.0055793221954760774, 0.48383536753337353, -0.00884339701416259, 0.006031476090923027, 0.5736456400487668, 0.006172158900414115, -0.00351058366549184, 1.2910336204097206e-05, -0.11119782368828623, -0.2625066647496439, -0.04674191819385598, -0.05323323494294563, 56.558651244179934, 57.63007331343629, 55.6176172458145, 42.08318106947576, 58.1002847513486, 51.40271597799311, 47.57773931072592, 44.1111126410454, 49.32490182611905, 57.59354869416344, 47.204660420850665, 51.30250451589714, 46.91928572493703, 39.66125799806949, 42.97167155803307, 54.54725448616931, 46.54019557341987, 51.88702981689826, 55.12202139627551, 53.31339458037436, 42.40094685983489, 55.624772754983574, 60.338313245611566, 63.148378099797405, 44.661938266959936, 48.86039298683612, 46.2576691889795, 39.05612090335749, 45.58027376561716, 41.804101630068345, 0.9499632647541526, 0.6614059906228291, 75.92570444129221, 1.0637479169572626, 0.7251822152497736, 79.92598348759616, 114.797436894087, 1.6289875982287716, 1.547403156958585, 1.0772529128598958, 0.8979240156970457, 123.6755723705694, 10.800291034351972, 1.733026624912506, 10.246594746874965, 1.1812204986176429, 1.814075643860278, 1.7232605455544905, 1.1998499219326693, 137.72766432165204, 1.929652473109375, 1.3155214520832406, 71.38072006259475, 0.6817416744133504, 1.3100116927814105, 0.9120126169692085, 104.7016914660845, 20.560518778741695, 9.154870504248546, 8.676311501134503, 0.9437705000000001, 0.65384575, 75.09012500000007, 1.0657262500000009, 0.7181029999999997, 79.56422499999998, 114.84804999999997, 1.6419229999999991, 1.5494455000000027, 1.0732922500000002, 0.9006339999999999, 123.27592500000006, 10.893041250000003, 1.7499434999999999, 10.298656250000008, 1.1789092499999994, 1.8230849999999996, 1.7204977499999998, 1.1918297500000006, 136.8840749999999, 1.9428619999999994, 1.3090529999999996, 70.46092500000016, 0.6738210000000002, 1.3143485000000008, 0.9104529999999995, 104.56730000000016, 20.82629400000003, 9.24522175000001, 8.736897749999997]\n",
            "adjcp:  30\n",
            "======Trading from:  20201117 to  20210120\n",
            "previous_total_asset:997224.0817484946\n",
            "end_total_asset:1012605.6403246098\n",
            "total_reward:15381.558576115174\n",
            "total_cost:  1254.5318193849996\n",
            "total trades:  898\n",
            "Sharpe:  0.2557382474788818\n",
            "previous_total_asset:997224.0817484946\n",
            "end_total_asset:1008534.0088519548\n",
            "total_reward:11309.927103460184\n",
            "total_cost:  415.359002232665\n",
            "total trades:  1066\n",
            "Sharpe:  0.21521048011725755\n",
            "previous_total_asset:997224.0817484946\n",
            "end_total_asset:1008315.2385674656\n",
            "total_reward:11091.156818970921\n",
            "total_cost:  1849.5607994054374\n",
            "total trades:  1309\n",
            "Sharpe:  0.20488685450263996\n",
            "previous_total_asset:997224.0817484946\n",
            "end_total_asset:1008477.9046359933\n",
            "total_reward:11253.822887498653\n",
            "total_cost:  1852.7134939361858\n",
            "total trades:  1310\n",
            "Sharpe:  0.2144517450267445\n",
            "previous_total_asset:997224.0817484946\n",
            "end_total_asset:1008710.3377396895\n",
            "total_reward:11486.255991194863\n",
            "total_cost:  2110.5605385619665\n",
            "total trades:  1341\n",
            "Sharpe:  0.21256347177811935\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.956825, 0.665285, 76.07300000000001, 1.05935, 0.73007, 79.518, 114.338, 1.62483, 1.5546, 1.0809549999999999, 0.895545, 123.6055, 10.761175, 1.72125, 10.240845, 1.18626, 1.81434, 1.736055, 1.207325, 138.04, 1.922, 1.3246, 71.8185, 0.689175, 1.31057, 0.91128, 104.2055, 20.26378, 9.069575, 8.6223, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.003191555350500863, 0.003816916150726324, 0.3370056343852639, -0.003918941973084511, 0.004205183221875908, 0.08822383631978425, -0.15412330865487434, -0.0067253144731427295, -0.0011288078588285178, 0.0018426112414784424, -0.0028688001660847062, 0.04605610238718327, -0.04491510191827608, -0.013538252161196862, -0.045529870510074844, 0.002051135742439758, -0.0017442031717020967, 0.004174600272392803, 0.005870769823895117, 0.48375232858154504, -0.008974147798999299, 0.006405654665404992, 0.5781115257678948, 0.006418653134130814, -0.003252214815806731, -3.20500261377088e-05, -0.14190956429735024, -0.2705467520712226, -0.05121264105276957, -0.054447579685882985, 56.32763388612609, 55.7930537929402, 53.1509783704637, 41.56835264412844, 56.88525281059283, 49.12002309521805, 46.43519099693078, 45.89911912999836, 51.03452594575586, 56.79619661401863, 46.18042893049508, 49.774550999092895, 47.72017555201188, 40.961313796941056, 44.397372540942236, 54.99976877804189, 49.602315101968955, 54.247335253717175, 55.86341084125788, 53.42978429013914, 44.79500877281034, 56.75849358433438, 57.98022005059295, 62.2298775573618, 46.064980278069086, 48.169104089544525, 44.33243483910359, 38.80954011969326, 45.84879151563761, 41.775183677520765, 0.9512108529806703, 0.6621112650550419, 75.95248545196635, 1.0629482956923058, 0.7260709033861784, 79.85180467166958, 114.7139029133439, 1.6282316712780858, 1.5487116738752058, 1.0779260196126421, 0.897491467388492, 123.66283193955678, 10.79317902810616, 1.7308854203829593, 10.245549338352244, 1.1821367715962532, 1.8141237086129547, 1.7255868099991285, 1.2012090270358202, 137.7844526268062, 1.9282611143622161, 1.317172097159015, 71.46031641485024, 0.6830931881563777, 1.3101132031847904, 0.9118794138838979, 104.61147483588732, 20.50656627351593, 9.13936223074881, 8.666491228200957, 0.944833, 0.65488625, 75.17195000000007, 1.0652132500000007, 0.7190217499999997, 79.56177499999998, 114.78974999999998, 1.6398242499999995, 1.549210250000003, 1.0736487500000003, 0.9003012499999998, 123.25320000000006, 10.884281000000005, 1.7468764999999997, 10.293230000000008, 1.1789202499999996, 1.8214177499999997, 1.7208675, 1.1926692500000005, 136.9087999999999, 1.9401519999999994, 1.3095387499999995, 70.57205000000016, 0.6750112500000003, 1.3141482500000008, 0.9107412499999995, 104.54782500000017, 20.78417050000003, 9.23769850000001, 8.731750999999997]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20201117\n",
            "======TD3 Training========\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 4         |\n",
            "| fps                     | 121       |\n",
            "| mean 100 episode reward | -4.6      |\n",
            "| n_updates               | 3000      |\n",
            "| qf1_loss                | 4348.797  |\n",
            "| qf2_loss                | 5436.9316 |\n",
            "| time_elapsed            | 26        |\n",
            "| total timesteps         | 3180      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 8         |\n",
            "| fps                     | 117       |\n",
            "| mean 100 episode reward | -4.6      |\n",
            "| n_updates               | 6200      |\n",
            "| qf1_loss                | 4058.9856 |\n",
            "| qf2_loss                | 2425.9473 |\n",
            "| time_elapsed            | 54        |\n",
            "| total timesteps         | 6360      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 117       |\n",
            "| mean 100 episode reward | -4.4      |\n",
            "| n_updates               | 9400      |\n",
            "| qf1_loss                | 5574.7075 |\n",
            "| qf2_loss                | 3916.2007 |\n",
            "| time_elapsed            | 81        |\n",
            "| total timesteps         | 9540      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 16        |\n",
            "| fps                     | 116       |\n",
            "| mean 100 episode reward | -4.2      |\n",
            "| n_updates               | 12600     |\n",
            "| qf1_loss                | 4942.5913 |\n",
            "| qf2_loss                | 1944.7699 |\n",
            "| time_elapsed            | 109       |\n",
            "| total timesteps         | 12720     |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 20       |\n",
            "| fps                     | 115      |\n",
            "| mean 100 episode reward | -4.1     |\n",
            "| n_updates               | 15800    |\n",
            "| qf1_loss                | 32881.81 |\n",
            "| qf2_loss                | 22133.78 |\n",
            "| time_elapsed            | 137      |\n",
            "| total timesteps         | 15900    |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 24        |\n",
            "| fps                     | 115       |\n",
            "| mean 100 episode reward | -3.8      |\n",
            "| n_updates               | 18900     |\n",
            "| qf1_loss                | 29266.162 |\n",
            "| qf2_loss                | 26108.873 |\n",
            "| time_elapsed            | 164       |\n",
            "| total timesteps         | 19080     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  2.902426258722941  minutes\n",
            "======TD3 Validation from:  20201117 to  20210120\n",
            "[0.23678358961555424, 0.979925, 0.683715, 79.9575, 1.0807, 0.769465, 81.595, 116.9445, 1.576125, 1.54451, 1.077795, 0.889825, 126.021, 10.362594999999999, 1.7037099999999998, 10.123875, 1.2128649999999999, 1.7711599999999998, 1.735675, 1.211395, 141.6205, 1.91395, 1.362945, 73.9985, 0.71215, 1.273475, 0.888445, 103.90100000000001, 19.6715, 8.54341, 8.34725, 4100.0, 4104.0, 0, 4100.0, 2500.0, 0, 4100.0, 0.0, 0, 0, 0, 0, 4100.0, 100.0, 4100.0, 2900.0, 4100.0, 0, 4129.0, 0, 4000.0, 0.0, 4022.0, 0, 4054.0, 4000.0, 100.0, 4100.0, 100.0, 486.0, 0.004046729024246076, 0.00422622902549985, 0.5420079852553812, 0.004452324071502645, 0.00503747058899473, 0.21903904722847756, 0.07228545308082346, -0.011515673961982609, -0.004708893266435865, -0.0010107076644352286, -0.0038588528346097384, -0.04358935856788548, -0.06573456379995868, -0.005267733797626573, -0.00857602410777858, -0.0006836641839189639, -0.005204147239764367, 0.0021838585853086734, 0.004062493221155705, 0.5561957328822871, 0.002320448929682195, 0.005007270996999491, 0.20153798013322444, 0.001773980164176514, -0.0032247564817218244, -0.0003963969428444347, 0.02127301224805933, -0.08249356247344508, -0.050067720590787346, -0.0028590376180375188, 57.686608375801214, 57.326372595533186, 59.265310051258396, 60.7531845424578, 58.72542766415849, 55.048433288521494, 54.07819452509855, 40.3905496333728, 46.48616787066266, 47.98032279407735, 44.304456811109965, 52.291190976210984, 42.1393270499079, 46.36245313956301, 47.06395880326003, 52.463831995314024, 46.18561082382142, 52.90025467961976, 54.50119064478793, 57.013640649302545, 52.021277262004126, 56.48687253681262, 54.46912867855964, 54.2846788009094, 44.07919662395077, 45.84695586591299, 49.17365941182768, 42.39565427161879, 43.23152234513063, 46.46750931250011, 0.9816387440901515, 0.6850664544970879, 80.02888606480819, 1.0772658161032378, 0.7712446803613378, 81.52775395238085, 116.81855264768049, 1.5746606320084602, 1.5457248800760266, 1.078822648190792, 0.8925747167044146, 126.0167648554063, 10.362625463876787, 1.6962350010258294, 10.113386170772417, 1.2144384798859207, 1.7643133709306926, 1.7318670022305644, 1.2087254509250793, 141.19260238459213, 1.9004897359221715, 1.3604904082496219, 74.2936045387294, 0.7159996807011596, 1.2727352643040764, 0.8882553546540752, 103.76406440700134, 19.79533874975362, 8.532782997664597, 8.3282460112821, 0.9811862500000001, 0.6823325, 79.75677500000006, 1.072750500000001, 0.7698947499999992, 81.28812499999998, 116.88820000000003, 1.5846417499999998, 1.554738750000003, 1.0812355000000005, 0.8977770000000005, 126.37900000000013, 10.4127245, 1.6998879999999996, 10.089675000000007, 1.2199354999999994, 1.7651977499999987, 1.731902250000001, 1.2043767500000007, 140.77999999999992, 1.8935317499999997, 1.3588114999999992, 74.34500000000017, 0.7177157500000002, 1.2744167500000005, 0.8862947499999997, 103.59375000000016, 19.88089450000003, 8.53516925000001, 8.271266500000001]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  0.07560060693470991\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.1993319153785706  minutes\n",
            "======PPO Validation from:  20201117 to  20210120\n",
            "[694804.5652877182, 0.979925, 0.683715, 79.9575, 1.0807, 0.769465, 81.595, 116.9445, 1.576125, 1.54451, 1.077795, 0.889825, 126.021, 10.362594999999999, 1.7037099999999998, 10.123875, 1.2128649999999999, 1.7711599999999998, 1.735675, 1.211395, 141.6205, 1.91395, 1.362945, 73.9985, 0.71215, 1.273475, 0.888445, 103.90100000000001, 19.6715, 8.54341, 8.34725, 2108.404876232147, 168.08001071214676, 109.86189126968384, 1047.263327717781, 0.0, 1863.62471139431, 507.5404998064041, 0.0, 0.0, 172.32867324352264, 0.0, 70.25237274169922, 136.32252609729767, 0.0, 398.1794122457504, 1274.1947400569916, 925.7433700561523, 1704.5475730895996, 0.0, 72.81055402755737, 1318.5382244586945, 1003.5812085866928, 37.99934387207031, 1270.4035730361938, 1423.875417470932, 122.19180679321289, 158.35024958848953, 981.6281095743179, 718.9822101593018, 0.0, 0.004046729024246076, 0.00422622902549985, 0.5420079852553812, 0.004452324071502645, 0.00503747058899473, 0.21903904722847756, 0.07228545308082346, -0.011515673961982609, -0.004708893266435865, -0.0010107076644352286, -0.0038588528346097384, -0.04358935856788548, -0.06573456379995868, -0.005267733797626573, -0.00857602410777858, -0.0006836641839189639, -0.005204147239764367, 0.0021838585853086734, 0.004062493221155705, 0.5561957328822871, 0.002320448929682195, 0.005007270996999491, 0.20153798013322444, 0.001773980164176514, -0.0032247564817218244, -0.0003963969428444347, 0.02127301224805933, -0.08249356247344508, -0.050067720590787346, -0.0028590376180375188, 57.686608375801214, 57.326372595533186, 59.265310051258396, 60.7531845424578, 58.72542766415849, 55.048433288521494, 54.07819452509855, 40.3905496333728, 46.48616787066266, 47.98032279407735, 44.304456811109965, 52.291190976210984, 42.1393270499079, 46.36245313956301, 47.06395880326003, 52.463831995314024, 46.18561082382142, 52.90025467961976, 54.50119064478793, 57.013640649302545, 52.021277262004126, 56.48687253681262, 54.46912867855964, 54.2846788009094, 44.07919662395077, 45.84695586591299, 49.17365941182768, 42.39565427161879, 43.23152234513063, 46.46750931250011, 0.9816387440901515, 0.6850664544970879, 80.02888606480819, 1.0772658161032378, 0.7712446803613378, 81.52775395238085, 116.81855264768049, 1.5746606320084602, 1.5457248800760266, 1.078822648190792, 0.8925747167044146, 126.0167648554063, 10.362625463876787, 1.6962350010258294, 10.113386170772417, 1.2144384798859207, 1.7643133709306926, 1.7318670022305644, 1.2087254509250793, 141.19260238459213, 1.9004897359221715, 1.3604904082496219, 74.2936045387294, 0.7159996807011596, 1.2727352643040764, 0.8882553546540752, 103.76406440700134, 19.79533874975362, 8.532782997664597, 8.3282460112821, 0.9811862500000001, 0.6823325, 79.75677500000006, 1.072750500000001, 0.7698947499999992, 81.28812499999998, 116.88820000000003, 1.5846417499999998, 1.554738750000003, 1.0812355000000005, 0.8977770000000005, 126.37900000000013, 10.4127245, 1.6998879999999996, 10.089675000000007, 1.2199354999999994, 1.7651977499999987, 1.731902250000001, 1.2043767500000007, 140.77999999999992, 1.8935317499999997, 1.3588114999999992, 74.34500000000017, 0.7177157500000002, 1.2744167500000005, 0.8862947499999997, 103.59375000000016, 19.88089450000003, 8.53516925000001, 8.271266500000001]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  0.05534560014315748\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.2331543644269307  minutes\n",
            "======DDPG Validation from:  20201117 to  20210120\n",
            "[0.13531937436101682, 0.979925, 0.683715, 79.9575, 1.0807, 0.769465, 81.595, 116.9445, 1.576125, 1.54451, 1.077795, 0.889825, 126.021, 10.362594999999999, 1.7037099999999998, 10.123875, 1.2128649999999999, 1.7711599999999998, 1.735675, 1.211395, 141.6205, 1.91395, 1.362945, 73.9985, 0.71215, 1.273475, 0.888445, 103.90100000000001, 19.6715, 8.54341, 8.34725, 4300.0, 4301.0, 4199.809951782227, 4199.918060302734, 4299.999977111816, 0, 0, 0, 0, 0, 0, 0, 4300.0, 4300.0, 4300.0, 4300.0, 4299.999977111816, 4300.0, 4300.0, 0, 4200.998565673828, 4199.983108520508, 0, 0, 4199.973350524902, 4300.0, 4232.0, 0, 4299.999412536621, 4205.999320983887, 0.004046729024246076, 0.00422622902549985, 0.5420079852553812, 0.004452324071502645, 0.00503747058899473, 0.21903904722847756, 0.07228545308082346, -0.011515673961982609, -0.004708893266435865, -0.0010107076644352286, -0.0038588528346097384, -0.04358935856788548, -0.06573456379995868, -0.005267733797626573, -0.00857602410777858, -0.0006836641839189639, -0.005204147239764367, 0.0021838585853086734, 0.004062493221155705, 0.5561957328822871, 0.002320448929682195, 0.005007270996999491, 0.20153798013322444, 0.001773980164176514, -0.0032247564817218244, -0.0003963969428444347, 0.02127301224805933, -0.08249356247344508, -0.050067720590787346, -0.0028590376180375188, 57.686608375801214, 57.326372595533186, 59.265310051258396, 60.7531845424578, 58.72542766415849, 55.048433288521494, 54.07819452509855, 40.3905496333728, 46.48616787066266, 47.98032279407735, 44.304456811109965, 52.291190976210984, 42.1393270499079, 46.36245313956301, 47.06395880326003, 52.463831995314024, 46.18561082382142, 52.90025467961976, 54.50119064478793, 57.013640649302545, 52.021277262004126, 56.48687253681262, 54.46912867855964, 54.2846788009094, 44.07919662395077, 45.84695586591299, 49.17365941182768, 42.39565427161879, 43.23152234513063, 46.46750931250011, 0.9816387440901515, 0.6850664544970879, 80.02888606480819, 1.0772658161032378, 0.7712446803613378, 81.52775395238085, 116.81855264768049, 1.5746606320084602, 1.5457248800760266, 1.078822648190792, 0.8925747167044146, 126.0167648554063, 10.362625463876787, 1.6962350010258294, 10.113386170772417, 1.2144384798859207, 1.7643133709306926, 1.7318670022305644, 1.2087254509250793, 141.19260238459213, 1.9004897359221715, 1.3604904082496219, 74.2936045387294, 0.7159996807011596, 1.2727352643040764, 0.8882553546540752, 103.76406440700134, 19.79533874975362, 8.532782997664597, 8.3282460112821, 0.9811862500000001, 0.6823325, 79.75677500000006, 1.072750500000001, 0.7698947499999992, 81.28812499999998, 116.88820000000003, 1.5846417499999998, 1.554738750000003, 1.0812355000000005, 0.8977770000000005, 126.37900000000013, 10.4127245, 1.6998879999999996, 10.089675000000007, 1.2199354999999994, 1.7651977499999987, 1.731902250000001, 1.2043767500000007, 140.77999999999992, 1.8935317499999997, 1.3588114999999992, 74.34500000000017, 0.7177157500000002, 1.2744167500000005, 0.8862947499999997, 103.59375000000016, 19.88089450000003, 8.53516925000001, 8.271266500000001]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 1.19e-07 |\n",
            "| fps                     | 14       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | -1.53    |\n",
            "| reference_Q_mean        | 0.62     |\n",
            "| reference_Q_std         | 0.919    |\n",
            "| reference_action_mean   | 0.267    |\n",
            "| reference_action_std    | 0.964    |\n",
            "| reference_actor_Q_mean  | 0.641    |\n",
            "| reference_actor_Q_std   | 1.01     |\n",
            "| rollout/Q_mean          | 0.493    |\n",
            "| rollout/actions_mean    | 0.123    |\n",
            "| rollout/actions_std     | 0.827    |\n",
            "| rollout/episode_steps   | 795      |\n",
            "| rollout/episodes        | 25       |\n",
            "| rollout/return          | -4.21    |\n",
            "| rollout/return_history  | -4.21    |\n",
            "| total/duration          | 133      |\n",
            "| total/episodes          | 25       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 150      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -0.87    |\n",
            "| train/loss_critic       | 0.0788   |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.0105   |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.041    |\n",
            "| fps                | 218      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | 1.48     |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 0.224    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0117  |\n",
            "| fps                | 230      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -0.405   |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 0.136    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.148    |\n",
            "| fps                | 236      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -0.988   |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 0.112    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.00882  |\n",
            "| fps                | 241      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | -2.38    |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.85     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.00709 |\n",
            "| fps                | 243      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 43.3     |\n",
            "| policy_loss        | -0.0271  |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 1.74     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.115   |\n",
            "| fps                | 243      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 43.3     |\n",
            "| policy_loss        | -0.513   |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 0.965    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 1.79e-07 |\n",
            "| fps                | 244      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | -1.77    |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 0.475    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.982   |\n",
            "| fps                | 244      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 43.5     |\n",
            "| policy_loss        | -0.923   |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 0.118    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0393  |\n",
            "| fps                | 244      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 43.5     |\n",
            "| policy_loss        | -0.254   |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 0.0631   |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0817  |\n",
            "| fps                | 244      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 43.6     |\n",
            "| policy_loss        | -0.248   |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 0.55     |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.421270219484965  minutes\n",
            "======ACKTR Validation from:  20201117 to  20210120\n",
            "[0.6383197161248972, 0.979925, 0.683715, 79.9575, 1.0807, 0.769465, 81.595, 116.9445, 1.576125, 1.54451, 1.077795, 0.889825, 126.021, 10.362594999999999, 1.7037099999999998, 10.123875, 1.2128649999999999, 1.7711599999999998, 1.735675, 1.211395, 141.6205, 1.91395, 1.362945, 73.9985, 0.71215, 1.273475, 0.888445, 103.90100000000001, 19.6715, 8.54341, 8.34725, 0.0, 0.0, 299.0559902191162, 2080.1748472452164, 1573.3436546325684, 2305.726388692856, 215.39112854003906, 1058.754245042801, 3968.975124359131, 315.94012546539307, 363.45823669433594, 1156.6764643192291, 2774.7145767211914, 0.0, 4409.855331420898, 91.01549530029297, 1552.8734996318817, 3468.5053417682648, 849.3938369750977, 148.87992477416992, 3640.945921421051, 165.32894897460938, 3394.48988956213, 2189.023772716522, 0.0, 1461.9250450134277, 1275.1796822547913, 3934.584806203842, 2551.836549758911, 1295.8428188562393, 0.004046729024246076, 0.00422622902549985, 0.5420079852553812, 0.004452324071502645, 0.00503747058899473, 0.21903904722847756, 0.07228545308082346, -0.011515673961982609, -0.004708893266435865, -0.0010107076644352286, -0.0038588528346097384, -0.04358935856788548, -0.06573456379995868, -0.005267733797626573, -0.00857602410777858, -0.0006836641839189639, -0.005204147239764367, 0.0021838585853086734, 0.004062493221155705, 0.5561957328822871, 0.002320448929682195, 0.005007270996999491, 0.20153798013322444, 0.001773980164176514, -0.0032247564817218244, -0.0003963969428444347, 0.02127301224805933, -0.08249356247344508, -0.050067720590787346, -0.0028590376180375188, 57.686608375801214, 57.326372595533186, 59.265310051258396, 60.7531845424578, 58.72542766415849, 55.048433288521494, 54.07819452509855, 40.3905496333728, 46.48616787066266, 47.98032279407735, 44.304456811109965, 52.291190976210984, 42.1393270499079, 46.36245313956301, 47.06395880326003, 52.463831995314024, 46.18561082382142, 52.90025467961976, 54.50119064478793, 57.013640649302545, 52.021277262004126, 56.48687253681262, 54.46912867855964, 54.2846788009094, 44.07919662395077, 45.84695586591299, 49.17365941182768, 42.39565427161879, 43.23152234513063, 46.46750931250011, 0.9816387440901515, 0.6850664544970879, 80.02888606480819, 1.0772658161032378, 0.7712446803613378, 81.52775395238085, 116.81855264768049, 1.5746606320084602, 1.5457248800760266, 1.078822648190792, 0.8925747167044146, 126.0167648554063, 10.362625463876787, 1.6962350010258294, 10.113386170772417, 1.2144384798859207, 1.7643133709306926, 1.7318670022305644, 1.2087254509250793, 141.19260238459213, 1.9004897359221715, 1.3604904082496219, 74.2936045387294, 0.7159996807011596, 1.2727352643040764, 0.8882553546540752, 103.76406440700134, 19.79533874975362, 8.532782997664597, 8.3282460112821, 0.9811862500000001, 0.6823325, 79.75677500000006, 1.072750500000001, 0.7698947499999992, 81.28812499999998, 116.88820000000003, 1.5846417499999998, 1.554738750000003, 1.0812355000000005, 0.8977770000000005, 126.37900000000013, 10.4127245, 1.6998879999999996, 10.089675000000007, 1.2199354999999994, 1.7651977499999987, 1.731902250000001, 1.2043767500000007, 140.77999999999992, 1.8935317499999997, 1.3588114999999992, 74.34500000000017, 0.7177157500000002, 1.2744167500000005, 0.8862947499999997, 103.59375000000016, 19.88089450000003, 8.53516925000001, 8.271266500000001]\n",
            "adjcp:  30\n",
            "======Trading from:  20210120 to  20210329\n",
            "previous_total_asset:1008710.3377396895\n",
            "end_total_asset:1021831.2861824587\n",
            "total_reward:13120.948442769237\n",
            "total_cost:  1318.9543279369518\n",
            "total trades:  990\n",
            "Sharpe:  0.18320182419982\n",
            "previous_total_asset:1008710.3377396895\n",
            "end_total_asset:1045123.2211663285\n",
            "total_reward:36412.88342663902\n",
            "total_cost:  1631.1998435840933\n",
            "total trades:  1026\n",
            "Sharpe:  0.8293387822110934\n",
            "previous_total_asset:1008710.3377396895\n",
            "end_total_asset:1045323.6196858286\n",
            "total_reward:36613.28194613906\n",
            "total_cost:  1967.848387465189\n",
            "total trades:  1339\n",
            "Sharpe:  0.5557320136456436\n",
            "previous_total_asset:1008710.3377396895\n",
            "end_total_asset:1050984.772850326\n",
            "total_reward:42274.43511063652\n",
            "total_cost:  2097.751229206537\n",
            "total trades:  1347\n",
            "Sharpe:  0.693214795132439\n",
            "previous_total_asset:1008710.3377396895\n",
            "end_total_asset:1045123.2211663285\n",
            "total_reward:36412.88342663902\n",
            "total_cost:  1631.1998435840933\n",
            "total trades:  1026\n",
            "Sharpe:  0.8293387822110934\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.9788, 0.68924, 80.2085, 1.0802049999999999, 0.77465, 81.947, 116.37700000000001, 1.56277, 1.52966, 1.0769799999999998, 0.886625, 125.3375, 10.28475, 1.68825, 10.10939, 1.2106, 1.7626099999999998, 1.72522, 1.214515, 141.3805, 1.9039, 1.365315, 74.2545, 0.717145, 1.263695, 0.8897200000000001, 103.55, 19.616575, 8.4955, 8.35135, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.003537490053813319, 0.004284032640113455, 0.5198190824525, 0.004420962947517815, 0.004958289461532428, 0.23854242907744094, 0.030521563870038904, -0.011742255672064328, -0.0057419052028171436, -0.0011105921186946865, -0.004121606128099087, -0.09915870444638131, -0.06771845526958487, -0.005553377915064273, -0.00809231597710891, -0.0010034090780604021, -0.004955264688800254, 0.0015218182659062496, 0.004291874997306211, 0.5363863217223752, 0.0025328184932924547, 0.005060423590460772, 0.18250728664169458, 0.0017126185376347136, -0.0037227621039210845, -0.00022723043756800454, 0.004283531621041448, -0.0918776564132564, -0.04940983775832386, -0.00023094393884370845, 56.8477765578512, 60.30371988107657, 60.368901990046886, 60.21816013470758, 60.854353477177526, 57.10044320617044, 50.2897074266757, 37.080901264811864, 41.618911580931936, 47.14615450687102, 42.765331259172484, 48.70216018319199, 39.56134349071581, 42.17630277338218, 46.26725419251267, 51.364850856726626, 43.83998703389552, 49.70780933276669, 55.57353219263763, 56.09341633884272, 49.57746478198359, 57.11497550883347, 55.82645575530992, 56.69034937432607, 40.50663799149287, 46.827340656862255, 46.647976634736295, 41.6889649286311, 41.8563021224547, 46.65938738942004, 0.981122608801033, 0.6858252809521629, 80.06154314393397, 1.0778002131753763, 0.771863829386549, 81.60398050649343, 116.73827034810222, 1.5724986989160128, 1.5428039927894763, 1.0784876212470118, 0.8914929500308846, 125.89326215442333, 10.348466288626462, 1.6947831826574966, 10.11265959426834, 1.213740574452117, 1.764003667125112, 1.7306584563704617, 1.2097780962114284, 141.22676558739354, 1.901109783936322, 1.3613676067496907, 74.28649462259679, 0.716207920573676, 1.2710915798851534, 0.8885216538078798, 103.72514360572836, 19.762836249798415, 8.52600427081649, 8.332446736503538, 0.9814700000000002, 0.68315025, 79.84342500000005, 1.0734117500000009, 0.7707502499999992, 81.35434999999998, 116.87732500000001, 1.5823369999999997, 1.5529372500000032, 1.0809340000000005, 0.8969500000000004, 126.33422500000013, 10.400170750000001, 1.6984152499999996, 10.089921750000006, 1.2195292499999995, 1.7642412499999989, 1.731473250000001, 1.2051650000000005, 140.86117499999992, 1.8936147499999998, 1.359603499999999, 74.38450000000016, 0.7180887500000002, 1.2733640000000004, 0.8863634999999996, 103.59262500000015, 19.85876275000003, 8.52769750000001, 8.274265000000002]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20210120\n",
            "======TD3 Training========\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 4         |\n",
            "| fps                     | 113       |\n",
            "| mean 100 episode reward | -4.8      |\n",
            "| n_updates               | 3200      |\n",
            "| qf1_loss                | 7521.913  |\n",
            "| qf2_loss                | 29924.947 |\n",
            "| time_elapsed            | 29        |\n",
            "| total timesteps         | 3380      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 8         |\n",
            "| fps                     | 111       |\n",
            "| mean 100 episode reward | -4.9      |\n",
            "| n_updates               | 6600      |\n",
            "| qf1_loss                | 18202.514 |\n",
            "| qf2_loss                | 18142.912 |\n",
            "| time_elapsed            | 60        |\n",
            "| total timesteps         | 6760      |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 12       |\n",
            "| fps                     | 110      |\n",
            "| mean 100 episode reward | -4.7     |\n",
            "| n_updates               | 10000    |\n",
            "| qf1_loss                | 13292.72 |\n",
            "| qf2_loss                | 9866.651 |\n",
            "| time_elapsed            | 92       |\n",
            "| total timesteps         | 10140    |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 16        |\n",
            "| fps                     | 109       |\n",
            "| mean 100 episode reward | -4.6      |\n",
            "| n_updates               | 13400     |\n",
            "| qf1_loss                | 33339.92  |\n",
            "| qf2_loss                | 16699.938 |\n",
            "| time_elapsed            | 123       |\n",
            "| total timesteps         | 13520     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 20        |\n",
            "| fps                     | 109       |\n",
            "| mean 100 episode reward | -4.6      |\n",
            "| n_updates               | 16800     |\n",
            "| qf1_loss                | 1521.5258 |\n",
            "| qf2_loss                | 2743.5823 |\n",
            "| time_elapsed            | 154       |\n",
            "| total timesteps         | 16900     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  3.055091937383016  minutes\n",
            "======TD3 Validation from:  20210120 to  20210329\n",
            "[0.7214627247512757, 0.9615, 0.717445, 83.831, 1.091935, 0.764355, 87.1985, 116.796, 1.5428950000000001, 1.482575, 1.10752, 0.855615, 129.3615, 10.1082, 1.6847599999999998, 10.1895, 1.1794200000000001, 1.8045400000000003, 1.7340099999999998, 1.29441, 151.2215, 1.970445, 1.379075, 76.773, 0.69999, 1.2578200000000002, 0.939045, 109.6765, 20.58145, 8.57035, 8.6386, 2101.0, 0, 2200.0, 2209.0, 0, 0, 2200.0, 0, 2200.0, 0, 2204.0, 2200.0, 0, 2200.0, 0, 0, 2100.0, 2230.0, 2100.0, 0, 0, 0.0, 0, 0, 0, 2100.0, 2100.0, 0, 2160.0, 2540.0, -0.0061834818503160305, 0.0030552456869507028, 0.28373009003446725, 0.004433199832733825, -0.003302645484859368, 0.8331245540651935, -0.10970765598483467, -0.0020718506495844835, -0.011634182507661572, 0.0034065014866515586, -0.0031147519058467577, 0.2744809914597397, -0.030107663174296917, 0.004592949411610814, 0.019707167021079997, -0.006684288344886324, 0.004136632806045926, -0.007133811767117582, 0.008436094618696766, 0.8457685687931473, 0.012388241873137584, -0.0027406068583732957, -0.0494050091121494, -0.0059462879412753855, -0.0026293683030260695, 0.007962359399480423, 0.8288636279876727, 0.04508852845905409, 0.022591044837787067, 0.06388780440761899, 43.535718319972474, 58.90240633012786, 57.99702150335718, 62.36561270809783, 47.72575419041075, 66.57930057095916, 49.29590120961991, 44.06665397934035, 36.04861976987312, 59.17018688768517, 37.46876689062181, 56.60586103493328, 43.58117382914429, 51.98338827178944, 54.33945745699501, 40.7035543726137, 53.98899801698584, 46.716350810578696, 64.03854588592402, 64.08602832646821, 60.484835216470344, 50.305328092311555, 52.09324856021044, 43.015881410965676, 46.35385506696713, 63.879445243681104, 70.35280082067867, 50.94322071855651, 50.46922005125214, 59.03345856021098, 0.9635980685232736, 0.7151649555329727, 83.5829710188005, 1.0852482152097125, 0.7674008935970733, 86.74421251911879, 116.86309138868351, 1.545908422137081, 1.4894930896410445, 1.1056995287062126, 0.8586444251906281, 129.20748570567764, 10.148650324974035, 1.6776154409411181, 10.165534269388676, 1.1863119959691306, 1.8005854398060344, 1.7348070252011185, 1.287795418288666, 150.4862094208956, 1.9540438955090715, 1.3817492179271862, 77.02700570685232, 0.707250814332995, 1.2556490138732823, 0.9320477392398935, 108.91468475091403, 20.690379054195727, 8.554788186821904, 8.569381350283152, 0.9703127500000001, 0.7163765, 83.75235000000006, 1.0791617500000004, 0.7719522499999989, 86.32509999999994, 116.91015, 1.5446547499999992, 1.4987180000000038, 1.1065275000000012, 0.8593977500000001, 129.36582500000011, 10.1537585, 1.6669457499999993, 10.151939750000011, 1.1924027499999998, 1.7974255, 1.7438890000000007, 1.2876330000000005, 150.53642499999995, 1.939776999999999, 1.3875284999999993, 77.61325000000014, 0.7154207500000004, 1.2568902500000008, 0.9280212500000002, 108.49327500000018, 20.79100750000003, 8.515390500000008, 8.514265000000004]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  0.3576819605141149\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.2468747893969219  minutes\n",
            "======PPO Validation from:  20210120 to  20210329\n",
            "[0.6966207794722356, 0.9615, 0.717445, 83.831, 1.091935, 0.764355, 87.1985, 116.796, 1.5428950000000001, 1.482575, 1.10752, 0.855615, 129.3615, 10.1082, 1.6847599999999998, 10.1895, 1.1794200000000001, 1.8045400000000003, 1.7340099999999998, 1.29441, 151.2215, 1.970445, 1.379075, 76.773, 0.69999, 1.2578200000000002, 0.939045, 109.6765, 20.58145, 8.57035, 8.6386, 1828.876091003418, 48.63652801513672, 1545.3097869157791, 0.0, 1.0, 2411.190637588501, 775.3579280376434, 0.0, 5.094857692718506, 0.0, 0.0, 1685.410903453827, 5.353961944580078, 0.0, 1019.4063172340393, 446.3383083343506, 0.0, 1519.1830234527588, 27.325136184692383, 965.2273664474487, 651.5343854427338, 195.19302001595497, 2345.940767288208, 26.069210529327393, 24.453231811523438, 1008.8124380111694, 0.0, 593.1854348182678, 0.0, 1068.8955948352814, -0.0061834818503160305, 0.0030552456869507028, 0.28373009003446725, 0.004433199832733825, -0.003302645484859368, 0.8331245540651935, -0.10970765598483467, -0.0020718506495844835, -0.011634182507661572, 0.0034065014866515586, -0.0031147519058467577, 0.2744809914597397, -0.030107663174296917, 0.004592949411610814, 0.019707167021079997, -0.006684288344886324, 0.004136632806045926, -0.007133811767117582, 0.008436094618696766, 0.8457685687931473, 0.012388241873137584, -0.0027406068583732957, -0.0494050091121494, -0.0059462879412753855, -0.0026293683030260695, 0.007962359399480423, 0.8288636279876727, 0.04508852845905409, 0.022591044837787067, 0.06388780440761899, 43.535718319972474, 58.90240633012786, 57.99702150335718, 62.36561270809783, 47.72575419041075, 66.57930057095916, 49.29590120961991, 44.06665397934035, 36.04861976987312, 59.17018688768517, 37.46876689062181, 56.60586103493328, 43.58117382914429, 51.98338827178944, 54.33945745699501, 40.7035543726137, 53.98899801698584, 46.716350810578696, 64.03854588592402, 64.08602832646821, 60.484835216470344, 50.305328092311555, 52.09324856021044, 43.015881410965676, 46.35385506696713, 63.879445243681104, 70.35280082067867, 50.94322071855651, 50.46922005125214, 59.03345856021098, 0.9635980685232736, 0.7151649555329727, 83.5829710188005, 1.0852482152097125, 0.7674008935970733, 86.74421251911879, 116.86309138868351, 1.545908422137081, 1.4894930896410445, 1.1056995287062126, 0.8586444251906281, 129.20748570567764, 10.148650324974035, 1.6776154409411181, 10.165534269388676, 1.1863119959691306, 1.8005854398060344, 1.7348070252011185, 1.287795418288666, 150.4862094208956, 1.9540438955090715, 1.3817492179271862, 77.02700570685232, 0.707250814332995, 1.2556490138732823, 0.9320477392398935, 108.91468475091403, 20.690379054195727, 8.554788186821904, 8.569381350283152, 0.9703127500000001, 0.7163765, 83.75235000000006, 1.0791617500000004, 0.7719522499999989, 86.32509999999994, 116.91015, 1.5446547499999992, 1.4987180000000038, 1.1065275000000012, 0.8593977500000001, 129.36582500000011, 10.1537585, 1.6669457499999993, 10.151939750000011, 1.1924027499999998, 1.7974255, 1.7438890000000007, 1.2876330000000005, 150.53642499999995, 1.939776999999999, 1.3875284999999993, 77.61325000000014, 0.7154207500000004, 1.2568902500000008, 0.9280212500000002, 108.49327500000018, 20.79100750000003, 8.515390500000008, 8.514265000000004]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  0.21342257499865003\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.252877926826477  minutes\n",
            "======DDPG Validation from:  20210120 to  20210329\n",
            "[137279.64043625857, 0.9615, 0.717445, 83.831, 1.091935, 0.764355, 87.1985, 116.796, 1.5428950000000001, 1.482575, 1.10752, 0.855615, 129.3615, 10.1082, 1.6847599999999998, 10.1895, 1.1794200000000001, 1.8045400000000003, 1.7340099999999998, 1.29441, 151.2215, 1.970445, 1.379075, 76.773, 0.69999, 1.2578200000000002, 0.939045, 109.6765, 20.58145, 8.57035, 8.6386, 4660.402488708496, 0, 0, 0, 0, 16.624736785888672, 4861.519149780273, 4899.999984741211, 4900.0, 0, 4897.886573791504, 0, 4831.404544830322, 4899.999984741211, 4899.9569091796875, 341.8803310394287, 0, 0, 0, 0, 4900.0, 4899.876068115234, 0.0, 4899.999977111816, 0, 0, 0, 4899.739059448242, 4899.999984741211, 0, -0.0061834818503160305, 0.0030552456869507028, 0.28373009003446725, 0.004433199832733825, -0.003302645484859368, 0.8331245540651935, -0.10970765598483467, -0.0020718506495844835, -0.011634182507661572, 0.0034065014866515586, -0.0031147519058467577, 0.2744809914597397, -0.030107663174296917, 0.004592949411610814, 0.019707167021079997, -0.006684288344886324, 0.004136632806045926, -0.007133811767117582, 0.008436094618696766, 0.8457685687931473, 0.012388241873137584, -0.0027406068583732957, -0.0494050091121494, -0.0059462879412753855, -0.0026293683030260695, 0.007962359399480423, 0.8288636279876727, 0.04508852845905409, 0.022591044837787067, 0.06388780440761899, 43.535718319972474, 58.90240633012786, 57.99702150335718, 62.36561270809783, 47.72575419041075, 66.57930057095916, 49.29590120961991, 44.06665397934035, 36.04861976987312, 59.17018688768517, 37.46876689062181, 56.60586103493328, 43.58117382914429, 51.98338827178944, 54.33945745699501, 40.7035543726137, 53.98899801698584, 46.716350810578696, 64.03854588592402, 64.08602832646821, 60.484835216470344, 50.305328092311555, 52.09324856021044, 43.015881410965676, 46.35385506696713, 63.879445243681104, 70.35280082067867, 50.94322071855651, 50.46922005125214, 59.03345856021098, 0.9635980685232736, 0.7151649555329727, 83.5829710188005, 1.0852482152097125, 0.7674008935970733, 86.74421251911879, 116.86309138868351, 1.545908422137081, 1.4894930896410445, 1.1056995287062126, 0.8586444251906281, 129.20748570567764, 10.148650324974035, 1.6776154409411181, 10.165534269388676, 1.1863119959691306, 1.8005854398060344, 1.7348070252011185, 1.287795418288666, 150.4862094208956, 1.9540438955090715, 1.3817492179271862, 77.02700570685232, 0.707250814332995, 1.2556490138732823, 0.9320477392398935, 108.91468475091403, 20.690379054195727, 8.554788186821904, 8.569381350283152, 0.9703127500000001, 0.7163765, 83.75235000000006, 1.0791617500000004, 0.7719522499999989, 86.32509999999994, 116.91015, 1.5446547499999992, 1.4987180000000038, 1.1065275000000012, 0.8593977500000001, 129.36582500000011, 10.1537585, 1.6669457499999993, 10.151939750000011, 1.1924027499999998, 1.7974255, 1.7438890000000007, 1.2876330000000005, 150.53642499999995, 1.939776999999999, 1.3875284999999993, 77.61325000000014, 0.7154207500000004, 1.2568902500000008, 0.9280212500000002, 108.49327500000018, 20.79100750000003, 8.515390500000008, 8.514265000000004]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 0        |\n",
            "| fps                     | 13       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | -0.271   |\n",
            "| reference_Q_mean        | 0.513    |\n",
            "| reference_Q_std         | 0.919    |\n",
            "| reference_action_mean   | -0.0409  |\n",
            "| reference_action_std    | 0.976    |\n",
            "| reference_actor_Q_mean  | 0.614    |\n",
            "| reference_actor_Q_std   | 1.1      |\n",
            "| rollout/Q_mean          | 0.314    |\n",
            "| rollout/actions_mean    | -0.064   |\n",
            "| rollout/actions_std     | 0.819    |\n",
            "| rollout/episode_steps   | 845      |\n",
            "| rollout/episodes        | 23       |\n",
            "| rollout/return          | -0.523   |\n",
            "| rollout/return_history  | -0.523   |\n",
            "| total/duration          | 134      |\n",
            "| total/episodes          | 23       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 149      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -0.655   |\n",
            "| train/loss_critic       | 0.0896   |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.0157   |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.802   |\n",
            "| fps                | 195      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | 0.197    |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 0.38     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.098    |\n",
            "| fps                | 221      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | -0.189   |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 0.705    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -2.17    |\n",
            "| fps                | 229      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | 0.372    |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 0.33     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.107   |\n",
            "| fps                | 234      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | -0.209   |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.384    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.021   |\n",
            "| fps                | 238      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 43       |\n",
            "| policy_loss        | -0.192   |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 0.162    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.159    |\n",
            "| fps                | 242      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | 0.0411   |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 1.46     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.00318  |\n",
            "| fps                | 245      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | -1.01    |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 0.539    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.465   |\n",
            "| fps                | 247      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | -1.14    |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 0.107    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.017    |\n",
            "| fps                | 248      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 43.3     |\n",
            "| policy_loss        | -0.72    |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 0.733    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0375   |\n",
            "| fps                | 250      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 43.3     |\n",
            "| policy_loss        | -0.954   |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 0.297    |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.386165153980255  minutes\n",
            "======ACKTR Validation from:  20210120 to  20210329\n",
            "[303593.67951305903, 0.9615, 0.717445, 83.831, 1.091935, 0.764355, 87.1985, 116.796, 1.5428950000000001, 1.482575, 1.10752, 0.855615, 129.3615, 10.1082, 1.6847599999999998, 10.1895, 1.1794200000000001, 1.8045400000000003, 1.7340099999999998, 1.29441, 151.2215, 1.970445, 1.379075, 76.773, 0.69999, 1.2578200000000002, 0.939045, 109.6765, 20.58145, 8.57035, 8.6386, 0.0, 1848.751084804535, 349.10987758636475, 2121.077980995178, 2963.4942712783813, 972.8955030441284, 3598.9038281440735, 1199.0392792224884, 376.4396209716797, 0.0, 1356.7392053604126, 0.0, 1125.1621806621552, 620.8028354644775, 544.3032604455948, 3041.0231761932373, 79.54460144042969, 0.0, 64.35359191894531, 0.0, 0.0, 2986.734268426895, 0.0, 0.0, 943.61585521698, 0.0, 1148.9390523433685, 0.0, 541.744948387146, 0.0, -0.0061834818503160305, 0.0030552456869507028, 0.28373009003446725, 0.004433199832733825, -0.003302645484859368, 0.8331245540651935, -0.10970765598483467, -0.0020718506495844835, -0.011634182507661572, 0.0034065014866515586, -0.0031147519058467577, 0.2744809914597397, -0.030107663174296917, 0.004592949411610814, 0.019707167021079997, -0.006684288344886324, 0.004136632806045926, -0.007133811767117582, 0.008436094618696766, 0.8457685687931473, 0.012388241873137584, -0.0027406068583732957, -0.0494050091121494, -0.0059462879412753855, -0.0026293683030260695, 0.007962359399480423, 0.8288636279876727, 0.04508852845905409, 0.022591044837787067, 0.06388780440761899, 43.535718319972474, 58.90240633012786, 57.99702150335718, 62.36561270809783, 47.72575419041075, 66.57930057095916, 49.29590120961991, 44.06665397934035, 36.04861976987312, 59.17018688768517, 37.46876689062181, 56.60586103493328, 43.58117382914429, 51.98338827178944, 54.33945745699501, 40.7035543726137, 53.98899801698584, 46.716350810578696, 64.03854588592402, 64.08602832646821, 60.484835216470344, 50.305328092311555, 52.09324856021044, 43.015881410965676, 46.35385506696713, 63.879445243681104, 70.35280082067867, 50.94322071855651, 50.46922005125214, 59.03345856021098, 0.9635980685232736, 0.7151649555329727, 83.5829710188005, 1.0852482152097125, 0.7674008935970733, 86.74421251911879, 116.86309138868351, 1.545908422137081, 1.4894930896410445, 1.1056995287062126, 0.8586444251906281, 129.20748570567764, 10.148650324974035, 1.6776154409411181, 10.165534269388676, 1.1863119959691306, 1.8005854398060344, 1.7348070252011185, 1.287795418288666, 150.4862094208956, 1.9540438955090715, 1.3817492179271862, 77.02700570685232, 0.707250814332995, 1.2556490138732823, 0.9320477392398935, 108.91468475091403, 20.690379054195727, 8.554788186821904, 8.569381350283152, 0.9703127500000001, 0.7163765, 83.75235000000006, 1.0791617500000004, 0.7719522499999989, 86.32509999999994, 116.91015, 1.5446547499999992, 1.4987180000000038, 1.1065275000000012, 0.8593977500000001, 129.36582500000011, 10.1537585, 1.6669457499999993, 10.151939750000011, 1.1924027499999998, 1.7974255, 1.7438890000000007, 1.2876330000000005, 150.53642499999995, 1.939776999999999, 1.3875284999999993, 77.61325000000014, 0.7154207500000004, 1.2568902500000008, 0.9280212500000002, 108.49327500000018, 20.79100750000003, 8.515390500000008, 8.514265000000004]\n",
            "adjcp:  30\n",
            "======Trading from:  20210329 to  20210602\n",
            "previous_total_asset:1045123.2211663285\n",
            "end_total_asset:1042713.6825274166\n",
            "total_reward:-2409.5386389119085\n",
            "total_cost:  235.67646173440647\n",
            "total trades:  1093\n",
            "Sharpe:  -0.04741722670739877\n",
            "previous_total_asset:1045123.2211663285\n",
            "end_total_asset:1052591.4814714557\n",
            "total_reward:7468.260305127129\n",
            "total_cost:  2008.1476121745227\n",
            "total trades:  1258\n",
            "Sharpe:  0.15269749157015725\n",
            "previous_total_asset:1045123.2211663285\n",
            "end_total_asset:1043906.8371159381\n",
            "total_reward:-1216.3840503904503\n",
            "total_cost:  1320.3447043312983\n",
            "total trades:  1348\n",
            "Sharpe:  -0.02314596440060169\n",
            "previous_total_asset:1045123.2211663285\n",
            "end_total_asset:1044082.2377679667\n",
            "total_reward:-1040.98339836183\n",
            "total_cost:  769.5303264878263\n",
            "total trades:  1275\n",
            "Sharpe:  -0.020014894124570654\n",
            "previous_total_asset:1045123.2211663285\n",
            "end_total_asset:1042713.6825274166\n",
            "total_reward:-2409.5386389119085\n",
            "total_cost:  235.67646173440647\n",
            "total trades:  1093\n",
            "Sharpe:  -0.04741722670739877\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.9612649999999999, 0.716375, 83.8065, 1.090695, 0.763145, 87.204, 116.979, 1.54151, 1.48128, 1.10507, 0.8549700000000001, 129.1765, 10.0612, 1.6811450000000001, 10.23334, 1.176505, 1.80349, 1.7327549999999998, 1.2924, 151.096, 1.966345, 1.3762, 76.842, 0.699865, 1.2591, 0.939205, 109.7975, 20.62615, 8.5519, 8.697275, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.006011847639972978, 0.0029265696075174708, 0.27862095557222233, 0.0046217846054370515, -0.0034757381028041046, 0.814940920219712, -0.09536853675204782, -0.002248099022627681, -0.011563484794153256, 0.0031229360123139482, -0.003195941707034522, 0.2505029271881938, -0.03500668283429853, 0.004684067353547761, 0.023966570323880276, -0.007076055187667629, 0.004128312420336622, -0.0068865637308856975, 0.008228145851577828, 0.8336990549817358, 0.012671305376383168, -0.0030701365043388495, -0.0681243870426016, -0.006221134793009786, -0.0021615095729790035, 0.008040256668752854, 0.8463755272187399, 0.03634740646353052, 0.021309461097837712, 0.07036503941276173, 43.429925365872656, 58.19678069529193, 57.868415257395, 61.232229660500614, 47.2171750132863, 66.60010711548676, 50.155313804587294, 43.661032301643324, 35.70218652762222, 57.174060135372734, 37.085699955463106, 55.597699218902285, 42.02509413491704, 50.88979036959725, 57.32296430942534, 39.68348710973267, 53.64941007535248, 46.35154655511098, 63.084751091444055, 63.55253716582766, 59.32880378581648, 49.26815164920629, 52.39253919100524, 42.967791309421116, 46.96213264609866, 63.93867289362427, 70.82747368519178, 51.4534436374811, 49.858115427922705, 61.148486907176306, 0.9631738742463148, 0.7153849636178868, 83.62361265174587, 1.0862385397170375, 0.7666270947612418, 86.82781024291538, 116.88416568165015, 1.545108709021248, 1.4879998006153998, 1.1055850689414468, 0.8579763478832413, 129.20185194100898, 10.132750265887847, 1.6782571789518241, 10.177862584045279, 1.184528905792925, 1.8011135416594828, 1.734433929710006, 1.288632614963454, 150.5970804352782, 1.9562804599619676, 1.3807402692131523, 76.99336830560644, 0.7059079389997232, 1.256276465896322, 0.9333490593780948, 109.07519661438421, 20.678701044341956, 8.554263061945193, 8.59263474114076, 0.9692280000000002, 0.7166272499999999, 83.79350000000007, 1.0802057500000006, 0.771249749999999, 86.46389999999994, 116.92624999999998, 1.5442187499999993, 1.4965952500000037, 1.1066510000000014, 0.8588775000000002, 129.3925750000001, 10.1411655, 1.6680827499999993, 10.15603425000001, 1.190983, 1.7980259999999997, 1.7424825000000006, 1.2885335000000004, 150.65772499999997, 1.942271499999999, 1.3867067499999994, 77.57687500000013, 0.7140857500000003, 1.2566177500000006, 0.9292217500000003, 108.64505000000017, 20.78968750000003, 8.514925500000007, 8.527893750000002]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20210329\n",
            "======TD3 Training========\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 4         |\n",
            "| fps                     | 117       |\n",
            "| mean 100 episode reward | 0.2       |\n",
            "| n_updates               | 3400      |\n",
            "| qf1_loss                | 59770.29  |\n",
            "| qf2_loss                | 53751.645 |\n",
            "| time_elapsed            | 30        |\n",
            "| total timesteps         | 3580      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 8         |\n",
            "| fps                     | 116       |\n",
            "| mean 100 episode reward | -0.1      |\n",
            "| n_updates               | 7000      |\n",
            "| qf1_loss                | 103893.94 |\n",
            "| qf2_loss                | 103626.81 |\n",
            "| time_elapsed            | 61        |\n",
            "| total timesteps         | 7160      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 114       |\n",
            "| mean 100 episode reward | -0.2      |\n",
            "| n_updates               | 10600     |\n",
            "| qf1_loss                | 112378.83 |\n",
            "| qf2_loss                | 102201.36 |\n",
            "| time_elapsed            | 93        |\n",
            "| total timesteps         | 10740     |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 16       |\n",
            "| fps                     | 113      |\n",
            "| mean 100 episode reward | -0.1     |\n",
            "| n_updates               | 14200    |\n",
            "| qf1_loss                | 81209.31 |\n",
            "| qf2_loss                | 66928.23 |\n",
            "| time_elapsed            | 126      |\n",
            "| total timesteps         | 14320    |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 20        |\n",
            "| fps                     | 112       |\n",
            "| mean 100 episode reward | -0.1      |\n",
            "| n_updates               | 17800     |\n",
            "| qf1_loss                | 24758.234 |\n",
            "| qf2_loss                | 45342.926 |\n",
            "| time_elapsed            | 159       |\n",
            "| total timesteps         | 17900     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  2.983751372496287  minutes\n",
            "======TD3 Validation from:  20210329 to  20210602\n",
            "[0.45229445528330814, 0.9359649999999999, 0.69558, 84.8835, 1.06844, 0.775365, 90.6975, 122.04899999999999, 1.57521, 1.474265, 1.095745, 0.863085, 133.70600000000002, 10.15814, 1.683195, 10.1044, 1.221315, 1.82512, 1.708115, 1.2698049999999999, 154.921, 1.949905, 1.414865, 79.4425, 0.725715, 1.2071399999999999, 0.89751, 109.4865, 19.9685, 8.3175, 8.2735, 2404.0, 4763.0, 0, 0, 0, 100.0, 2428.0, 2675.0, 2401.0, 0, 0.0, 0, 0, 0, 2400.0, 2401.0, 0, 0, 2400.0, 3773.0, 0, 0, 0.0, 2404.0, 0, 2400.0, 0, 2400.0, 0, 4900.0, -0.005839149456431492, -0.0034148286435810027, 0.12012604655349435, -0.0033722819613453137, -0.00010631343505240753, 0.6736908035675668, 0.7586750267232816, 0.006241939924987117, -0.0032013206530892013, -0.0009449586290386502, -0.0009108669305161188, 0.7137764879541066, 0.03960895217374727, 0.0014587045953691202, -0.00811970493364278, 0.00466973720942776, 0.009150765316747078, -0.0018797613940795266, 0.00024503878999482254, 0.9918450416633391, 0.0037603106037336076, 0.006862186983323637, 0.358462922299708, 0.0021622798444193014, -0.007379364620339279, -0.004282297170432048, 0.16915012826912346, -0.038985915011384265, 0.00015123601948374699, -0.039207484044013086, 41.551626475236986, 43.911790755700565, 55.73302484167791, 44.60853556783938, 51.14873066898954, 64.827159116819, 64.23802329065006, 55.40219145903901, 44.49059906728166, 48.08759060182965, 48.65585241455903, 65.34223690498206, 52.238230600720705, 50.8335669459602, 47.168729254485164, 56.47746986514294, 55.341920372874434, 45.717435516358904, 49.99232896583503, 61.9506422882577, 51.440342442882866, 56.78171734314726, 57.04098683859418, 53.16607739135268, 37.67791113822874, 42.73838131089552, 55.51144173951302, 47.6649778047264, 47.7059341987833, 44.38913704986088, 0.9358840829433668, 0.696249660386099, 84.70082704877812, 1.0688654439930778, 0.7745722155038457, 90.5020562497439, 121.65720112547166, 1.574825480223333, 1.4739017416246822, 1.0965105561907114, 0.8616253298199877, 133.38590651583692, 10.16022132668982, 1.683429348714658, 10.127515712310478, 1.219832220345125, 1.8277383201275634, 1.7106412010159968, 1.2725405438288937, 154.8090068485482, 1.9537928917240535, 1.4156478725543526, 79.24371760047191, 0.7246517998166652, 1.2082803029930158, 0.8989305567513729, 109.3498745636942, 19.93549604224398, 8.328709586112197, 8.302519713347653, 0.9395632500000002, 0.6997220000000001, 84.74857500000009, 1.073408250000001, 0.7763424999999993, 90.20037499999997, 121.12039999999998, 1.5666292499999985, 1.4719330000000035, 1.096203500000001, 0.8619330000000008, 132.7681750000001, 10.106639, 1.6817274999999992, 10.135897000000012, 1.2162639999999998, 1.8175782499999997, 1.7076942500000005, 1.2717605000000014, 154.03044999999995, 1.9510987499999994, 1.4110247499999997, 78.95667500000016, 0.7232592500000006, 1.2102395000000006, 0.9013250000000002, 109.16090000000015, 19.949556000000033, 8.309422500000004, 8.333826750000002]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  0.4587907089199762\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.247710676987966  minutes\n",
            "======PPO Validation from:  20210329 to  20210602\n",
            "[126913.27454624974, 0.9359649999999999, 0.69558, 84.8835, 1.06844, 0.775365, 90.6975, 122.04899999999999, 1.57521, 1.474265, 1.095745, 0.863085, 133.70600000000002, 10.15814, 1.683195, 10.1044, 1.221315, 1.82512, 1.708115, 1.2698049999999999, 154.921, 1.949905, 1.414865, 79.4425, 0.725715, 1.2071399999999999, 0.89751, 109.4865, 19.9685, 8.3175, 8.2735, 287.6878328323364, 123.94442558288574, 329.5684070587158, 0.0, 64.13458824157715, 1861.9746615886688, 186.7063331604004, 377.4004817008972, 100.0, 224.5751256942749, 0.0, 438.7774770259857, 696.5329603552818, 90.2827377319336, 0.0, 0.0, 1938.409294128418, 1724.562243938446, 301.0820255279541, 1762.039247751236, 0.0, 0.0, 36.939353942871094, 79.89527702331543, 0.0, 971.419554233551, 2786.857527732849, 8.668606758117676, 962.7255616188049, 115.2924575805664, -0.005839149456431492, -0.0034148286435810027, 0.12012604655349435, -0.0033722819613453137, -0.00010631343505240753, 0.6736908035675668, 0.7586750267232816, 0.006241939924987117, -0.0032013206530892013, -0.0009449586290386502, -0.0009108669305161188, 0.7137764879541066, 0.03960895217374727, 0.0014587045953691202, -0.00811970493364278, 0.00466973720942776, 0.009150765316747078, -0.0018797613940795266, 0.00024503878999482254, 0.9918450416633391, 0.0037603106037336076, 0.006862186983323637, 0.358462922299708, 0.0021622798444193014, -0.007379364620339279, -0.004282297170432048, 0.16915012826912346, -0.038985915011384265, 0.00015123601948374699, -0.039207484044013086, 41.551626475236986, 43.911790755700565, 55.73302484167791, 44.60853556783938, 51.14873066898954, 64.827159116819, 64.23802329065006, 55.40219145903901, 44.49059906728166, 48.08759060182965, 48.65585241455903, 65.34223690498206, 52.238230600720705, 50.8335669459602, 47.168729254485164, 56.47746986514294, 55.341920372874434, 45.717435516358904, 49.99232896583503, 61.9506422882577, 51.440342442882866, 56.78171734314726, 57.04098683859418, 53.16607739135268, 37.67791113822874, 42.73838131089552, 55.51144173951302, 47.6649778047264, 47.7059341987833, 44.38913704986088, 0.9358840829433668, 0.696249660386099, 84.70082704877812, 1.0688654439930778, 0.7745722155038457, 90.5020562497439, 121.65720112547166, 1.574825480223333, 1.4739017416246822, 1.0965105561907114, 0.8616253298199877, 133.38590651583692, 10.16022132668982, 1.683429348714658, 10.127515712310478, 1.219832220345125, 1.8277383201275634, 1.7106412010159968, 1.2725405438288937, 154.8090068485482, 1.9537928917240535, 1.4156478725543526, 79.24371760047191, 0.7246517998166652, 1.2082803029930158, 0.8989305567513729, 109.3498745636942, 19.93549604224398, 8.328709586112197, 8.302519713347653, 0.9395632500000002, 0.6997220000000001, 84.74857500000009, 1.073408250000001, 0.7763424999999993, 90.20037499999997, 121.12039999999998, 1.5666292499999985, 1.4719330000000035, 1.096203500000001, 0.8619330000000008, 132.7681750000001, 10.106639, 1.6817274999999992, 10.135897000000012, 1.2162639999999998, 1.8175782499999997, 1.7076942500000005, 1.2717605000000014, 154.03044999999995, 1.9510987499999994, 1.4110247499999997, 78.95667500000016, 0.7232592500000006, 1.2102395000000006, 0.9013250000000002, 109.16090000000015, 19.949556000000033, 8.309422500000004, 8.333826750000002]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  0.3152745881942834\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.227550796667735  minutes\n",
            "======DDPG Validation from:  20210329 to  20210602\n",
            "[0.10302759612187473, 0.9359649999999999, 0.69558, 84.8835, 1.06844, 0.775365, 90.6975, 122.04899999999999, 1.57521, 1.474265, 1.095745, 0.863085, 133.70600000000002, 10.15814, 1.683195, 10.1044, 1.221315, 1.82512, 1.708115, 1.2698049999999999, 154.921, 1.949905, 1.414865, 79.4425, 0.725715, 1.2071399999999999, 0.89751, 109.4865, 19.9685, 8.3175, 8.2735, 2581.9977645874023, 0, 2600.0, 2599.999465942383, 0, 0, 2499.9506072998047, 2599.9986114501953, 0, 2500.9853591918945, 2499.956474304199, 0, 2499.990852355957, 0, 0, 2498.997184753418, 2600.0, 0, 2599.999984741211, 2507.998779296875, 2600.0, 2599.999168395996, 0, 2499.9021606445312, 0, 2600.0, 0, 0, 2599.999656677246, 2600.0, -0.005839149456431492, -0.0034148286435810027, 0.12012604655349435, -0.0033722819613453137, -0.00010631343505240753, 0.6736908035675668, 0.7586750267232816, 0.006241939924987117, -0.0032013206530892013, -0.0009449586290386502, -0.0009108669305161188, 0.7137764879541066, 0.03960895217374727, 0.0014587045953691202, -0.00811970493364278, 0.00466973720942776, 0.009150765316747078, -0.0018797613940795266, 0.00024503878999482254, 0.9918450416633391, 0.0037603106037336076, 0.006862186983323637, 0.358462922299708, 0.0021622798444193014, -0.007379364620339279, -0.004282297170432048, 0.16915012826912346, -0.038985915011384265, 0.00015123601948374699, -0.039207484044013086, 41.551626475236986, 43.911790755700565, 55.73302484167791, 44.60853556783938, 51.14873066898954, 64.827159116819, 64.23802329065006, 55.40219145903901, 44.49059906728166, 48.08759060182965, 48.65585241455903, 65.34223690498206, 52.238230600720705, 50.8335669459602, 47.168729254485164, 56.47746986514294, 55.341920372874434, 45.717435516358904, 49.99232896583503, 61.9506422882577, 51.440342442882866, 56.78171734314726, 57.04098683859418, 53.16607739135268, 37.67791113822874, 42.73838131089552, 55.51144173951302, 47.6649778047264, 47.7059341987833, 44.38913704986088, 0.9358840829433668, 0.696249660386099, 84.70082704877812, 1.0688654439930778, 0.7745722155038457, 90.5020562497439, 121.65720112547166, 1.574825480223333, 1.4739017416246822, 1.0965105561907114, 0.8616253298199877, 133.38590651583692, 10.16022132668982, 1.683429348714658, 10.127515712310478, 1.219832220345125, 1.8277383201275634, 1.7106412010159968, 1.2725405438288937, 154.8090068485482, 1.9537928917240535, 1.4156478725543526, 79.24371760047191, 0.7246517998166652, 1.2082803029930158, 0.8989305567513729, 109.3498745636942, 19.93549604224398, 8.328709586112197, 8.302519713347653, 0.9395632500000002, 0.6997220000000001, 84.74857500000009, 1.073408250000001, 0.7763424999999993, 90.20037499999997, 121.12039999999998, 1.5666292499999985, 1.4719330000000035, 1.096203500000001, 0.8619330000000008, 132.7681750000001, 10.106639, 1.6817274999999992, 10.135897000000012, 1.2162639999999998, 1.8175782499999997, 1.7076942500000005, 1.2717605000000014, 154.03044999999995, 1.9510987499999994, 1.4110247499999997, 78.95667500000016, 0.7232592500000006, 1.2102395000000006, 0.9013250000000002, 109.16090000000015, 19.949556000000033, 8.309422500000004, 8.333826750000002]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 0        |\n",
            "| fps                     | 16       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | 0.0407   |\n",
            "| reference_Q_mean        | 0.656    |\n",
            "| reference_Q_std         | 0.417    |\n",
            "| reference_action_mean   | 0.2      |\n",
            "| reference_action_std    | 0.98     |\n",
            "| reference_actor_Q_mean  | 0.758    |\n",
            "| reference_actor_Q_std   | 0.397    |\n",
            "| rollout/Q_mean          | 0.371    |\n",
            "| rollout/actions_mean    | -0.0067  |\n",
            "| rollout/actions_std     | 0.838    |\n",
            "| rollout/episode_steps   | 895      |\n",
            "| rollout/episodes        | 22       |\n",
            "| rollout/return          | -2.15    |\n",
            "| rollout/return_history  | -2.15    |\n",
            "| total/duration          | 133      |\n",
            "| total/episodes          | 22       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 151      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -0.643   |\n",
            "| train/loss_critic       | 0.114    |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.0104   |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0388  |\n",
            "| fps                | 239      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | 0.0578   |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 1.36     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.51    |\n",
            "| fps                | 249      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | 0.925    |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 0.575    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.00983  |\n",
            "| fps                | 254      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | -0.43    |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 0.785    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0729  |\n",
            "| fps                | 256      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 43.3     |\n",
            "| policy_loss        | -0.742   |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.412    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.125   |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | -1.46    |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 0.647    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.441   |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | 0.205    |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 0.221    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0521  |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 43.5     |\n",
            "| policy_loss        | -0.458   |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 1.25     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0209   |\n",
            "| fps                | 256      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 43.5     |\n",
            "| policy_loss        | -0.794   |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 0.532    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0612  |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 43.6     |\n",
            "| policy_loss        | -0.235   |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 2.12     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0746   |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 43.5     |\n",
            "| policy_loss        | -0.787   |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 0.117    |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.3528813163439433  minutes\n",
            "======ACKTR Validation from:  20210329 to  20210602\n",
            "[48029.19763285818, 0.9359649999999999, 0.69558, 84.8835, 1.06844, 0.775365, 90.6975, 122.04899999999999, 1.57521, 1.474265, 1.095745, 0.863085, 133.70600000000002, 10.15814, 1.683195, 10.1044, 1.221315, 1.82512, 1.708115, 1.2698049999999999, 154.921, 1.949905, 1.414865, 79.4425, 0.725715, 1.2071399999999999, 0.89751, 109.4865, 19.9685, 8.3175, 8.2735, 3951.1006603240967, 2364.767324924469, 77.98058319091797, 1636.8395862579346, 3408.4671902656555, 0.0, 3012.4834208488464, 1231.745493888855, 252.74706196784973, 1450.592770934105, 1299.894690990448, 1761.2585124969482, 1185.9807631969452, 3172.3387398719788, 49.066932678222656, 2217.708236694336, 327.1562023162842, 4835.675765991211, 1423.7028894424438, 1752.35325050354, 1297.3149907588959, 1129.7621219158173, 42.73780822753906, 135.60458374023438, 1918.5132431983948, 1129.6397211551666, 202.20249938964844, 82.30012130737305, 94.07011461257935, 380.41368705034256, -0.005839149456431492, -0.0034148286435810027, 0.12012604655349435, -0.0033722819613453137, -0.00010631343505240753, 0.6736908035675668, 0.7586750267232816, 0.006241939924987117, -0.0032013206530892013, -0.0009449586290386502, -0.0009108669305161188, 0.7137764879541066, 0.03960895217374727, 0.0014587045953691202, -0.00811970493364278, 0.00466973720942776, 0.009150765316747078, -0.0018797613940795266, 0.00024503878999482254, 0.9918450416633391, 0.0037603106037336076, 0.006862186983323637, 0.358462922299708, 0.0021622798444193014, -0.007379364620339279, -0.004282297170432048, 0.16915012826912346, -0.038985915011384265, 0.00015123601948374699, -0.039207484044013086, 41.551626475236986, 43.911790755700565, 55.73302484167791, 44.60853556783938, 51.14873066898954, 64.827159116819, 64.23802329065006, 55.40219145903901, 44.49059906728166, 48.08759060182965, 48.65585241455903, 65.34223690498206, 52.238230600720705, 50.8335669459602, 47.168729254485164, 56.47746986514294, 55.341920372874434, 45.717435516358904, 49.99232896583503, 61.9506422882577, 51.440342442882866, 56.78171734314726, 57.04098683859418, 53.16607739135268, 37.67791113822874, 42.73838131089552, 55.51144173951302, 47.6649778047264, 47.7059341987833, 44.38913704986088, 0.9358840829433668, 0.696249660386099, 84.70082704877812, 1.0688654439930778, 0.7745722155038457, 90.5020562497439, 121.65720112547166, 1.574825480223333, 1.4739017416246822, 1.0965105561907114, 0.8616253298199877, 133.38590651583692, 10.16022132668982, 1.683429348714658, 10.127515712310478, 1.219832220345125, 1.8277383201275634, 1.7106412010159968, 1.2725405438288937, 154.8090068485482, 1.9537928917240535, 1.4156478725543526, 79.24371760047191, 0.7246517998166652, 1.2082803029930158, 0.8989305567513729, 109.3498745636942, 19.93549604224398, 8.328709586112197, 8.302519713347653, 0.9395632500000002, 0.6997220000000001, 84.74857500000009, 1.073408250000001, 0.7763424999999993, 90.20037499999997, 121.12039999999998, 1.5666292499999985, 1.4719330000000035, 1.096203500000001, 0.8619330000000008, 132.7681750000001, 10.106639, 1.6817274999999992, 10.135897000000012, 1.2162639999999998, 1.8175782499999997, 1.7076942500000005, 1.2717605000000014, 154.03044999999995, 1.9510987499999994, 1.4110247499999997, 78.95667500000016, 0.7232592500000006, 1.2102395000000006, 0.9013250000000002, 109.16090000000015, 19.949556000000033, 8.309422500000004, 8.333826750000002]\n",
            "adjcp:  30\n",
            "======Trading from:  20210602 to  20210811\n",
            "previous_total_asset:1042713.6825274166\n",
            "end_total_asset:1039971.8532420355\n",
            "total_reward:-2741.829285381129\n",
            "total_cost:  1877.2672439609348\n",
            "total trades:  1082\n",
            "Sharpe:  -0.03465563259217176\n",
            "previous_total_asset:1042713.6825274166\n",
            "end_total_asset:1033822.2017658268\n",
            "total_reward:-8891.480761589832\n",
            "total_cost:  968.7905215109591\n",
            "total trades:  1082\n",
            "Sharpe:  -0.11596927131391832\n",
            "previous_total_asset:1042713.6825274166\n",
            "end_total_asset:1040242.834103429\n",
            "total_reward:-2470.848423987627\n",
            "total_cost:  1014.2000025652361\n",
            "total trades:  1302\n",
            "Sharpe:  -0.031568654179788096\n",
            "previous_total_asset:1042713.6825274166\n",
            "end_total_asset:1036923.9487830475\n",
            "total_reward:-5789.733744369121\n",
            "total_cost:  1372.0991245412931\n",
            "total trades:  1330\n",
            "Sharpe:  -0.07858877010205614\n",
            "previous_total_asset:1042713.6825274166\n",
            "end_total_asset:1039971.8532420355\n",
            "total_reward:-2741.829285381129\n",
            "total_cost:  1877.2672439609348\n",
            "total trades:  1082\n",
            "Sharpe:  -0.03465563259217176\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.9331200000000001, 0.696195, 84.9495, 1.07111, 0.775385, 91.042, 122.0215, 1.574935, 1.46954, 1.096295, 0.8617299999999999, 133.791, 10.13085, 1.687275, 10.09478, 1.22108, 1.827575, 1.70537, 1.2722149999999999, 155.262, 1.957745, 1.417115, 79.3025, 0.7238100000000001, 1.203445, 0.897885, 109.556, 19.900389999999998, 8.2964, 8.265805, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.005687427442348181, -0.003209232250179772, 0.13211318655429238, -0.0030055022291428646, -4.59776405904444e-05, 0.6736707740017209, 0.7404874994629154, 0.005877684927575677, -0.003322155082135181, -0.0008966194375104042, -0.0008459826085149835, 0.7023597987765697, 0.034984344004845624, 0.0016563223817021022, -0.010260858019091046, 0.004472925296559183, 0.00858653810474097, -0.0021501046597169893, 0.0002120983946143351, 0.9671450226004197, 0.0038204593978963253, 0.0065456655067097635, 0.34210305667544105, 0.0019651350128647715, -0.007276566509905491, -0.0040889348759439725, 0.17615696618392462, -0.03903363229345658, -0.002245980123390723, -0.0396826726312991, 40.357183821585416, 44.4928179717132, 56.117568457847035, 46.810293035193084, 51.15913268024423, 66.25021685875583, 64.03584626253141, 55.271824519490295, 42.84254214140514, 48.5725506119023, 47.60478738525499, 65.71116284406926, 50.73594110207586, 52.014505494180305, 46.53370913778476, 56.34685873581796, 55.982918728392754, 44.86660042896503, 51.12461146355447, 62.931164718115596, 53.062415603526496, 57.51795599020475, 56.19568900941074, 52.225544626558296, 36.32701820729067, 43.01043863357875, 55.92074020520996, 46.4537536318225, 46.907096574927515, 44.082800991026176, 0.9353815224082092, 0.696239722134081, 84.74604031263665, 1.0692735450852455, 0.7747199945031464, 90.60022784069956, 121.72343728447682, 1.57484539291, 1.473108697692922, 1.0964713641560364, 0.8616443607618082, 133.45955987659383, 10.15488108547349, 1.684128558039266, 10.121563764617663, 1.2200590893732841, 1.8277086255589154, 1.70968280083127, 1.2724813540418223, 154.89136923972126, 1.954511456865135, 1.4159146229990158, 79.25440530947702, 0.7244987453045443, 1.2074011569942857, 0.8987404555238504, 109.3873519157498, 19.929113125472348, 8.32283511590998, 8.295844310920806, 0.9387022500000001, 0.6991565000000001, 84.76567500000007, 1.073276500000001, 0.7763752499999993, 90.30082499999996, 121.24219999999995, 1.5679049999999983, 1.4717795000000033, 1.096218250000001, 0.8618565000000009, 132.9029000000001, 10.111736500000001, 1.682899499999999, 10.131199000000013, 1.2172939999999999, 1.81921, 1.7076777500000007, 1.2718675000000013, 154.20059999999995, 1.9526202499999996, 1.4123432499999997, 78.98197500000018, 0.7233755000000006, 1.2090760000000007, 0.9005610000000001, 109.17857500000017, 19.931456000000033, 8.306532500000005, 8.322754500000002]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20210602\n",
            "======TD3 Training========\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 4         |\n",
            "| fps                     | 114       |\n",
            "| mean 100 episode reward | 1.2       |\n",
            "| n_updates               | 3600      |\n",
            "| qf1_loss                | 657527.25 |\n",
            "| qf2_loss                | 907523.8  |\n",
            "| time_elapsed            | 33        |\n",
            "| total timesteps         | 3780      |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 8        |\n",
            "| fps                     | 116      |\n",
            "| mean 100 episode reward | 0.9      |\n",
            "| n_updates               | 7400     |\n",
            "| qf1_loss                | 379983.0 |\n",
            "| qf2_loss                | 578371.9 |\n",
            "| time_elapsed            | 65       |\n",
            "| total timesteps         | 7560     |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 116       |\n",
            "| mean 100 episode reward | 0.8       |\n",
            "| n_updates               | 11200     |\n",
            "| qf1_loss                | 421377.53 |\n",
            "| qf2_loss                | 1109350.4 |\n",
            "| time_elapsed            | 97        |\n",
            "| total timesteps         | 11340     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 16        |\n",
            "| fps                     | 116       |\n",
            "| mean 100 episode reward | 0.7       |\n",
            "| n_updates               | 15000     |\n",
            "| qf1_loss                | 180150.69 |\n",
            "| qf2_loss                | 117758.14 |\n",
            "| time_elapsed            | 130       |\n",
            "| total timesteps         | 15120     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 20        |\n",
            "| fps                     | 116       |\n",
            "| mean 100 episode reward | 0.7       |\n",
            "| n_updates               | 18800     |\n",
            "| qf1_loss                | 143598.17 |\n",
            "| qf2_loss                | 265966.0  |\n",
            "| time_elapsed            | 162       |\n",
            "| total timesteps         | 18900     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  2.8899088859558106  minutes\n",
            "======TD3 Validation from:  20210602 to  20210811\n",
            "[0.49800903482803704, 0.9203, 0.677975, 81.2695, 1.04799, 0.73502, 88.30799999999999, 119.8765, 1.595065, 1.46738, 1.0815350000000001, 0.8469, 129.58100000000002, 10.45575, 1.671555, 10.21675, 1.1719950000000001, 1.8837700000000002, 1.7337650000000002, 1.276735, 153.0975, 1.974145, 1.38435, 77.5015, 0.701105, 1.251965, 0.922375, 110.57, 20.08544, 8.92135, 8.7174, 0, 0, 0, 0, 0, 1552.0, 1300.0, 0.0, 0, 0, 1408.0, 1386.0, 1304.0, 1531.0, 0, 0, 1300.0, 1300.0, 0, 1400.0, 1300.0, 1300.0, 1300.0, 1303.0, 1300.0, 1300.0, 1300.0, 1300.0, 0, 0.0, -0.0026990765381580406, -0.003685586233816429, -0.45305846582324705, -0.005164934630627949, -0.003920975387673997, -0.23523107996511783, -0.013221999880542512, 0.0039546081126944355, -0.0006225435163658144, -0.0031393420826704155, -0.002472604001438672, -0.4004995533421152, 0.04564540283850249, -0.004003805512231784, 0.008756649602331024, -0.0032909856497294054, 0.010032300071677769, 0.0043634653932753675, -9.573422783182295e-06, -0.019329306217343856, 0.0010603545544483062, 0.0002116577525026475, -0.05638345395317401, -0.0002950524943947652, 0.0029134073179482023, -0.00014103342797933127, -0.03147981872477601, 0.007489282819598486, 0.06249632292141705, 0.03115897991554739, 41.700528622906404, 44.571046916077094, 43.934672840216614, 36.88683652257833, 41.42211684431859, 49.94454548377505, 47.71020092818331, 53.24614836292338, 45.18002222685545, 45.06957044257896, 41.608810524541234, 44.29898426852809, 55.7574118364109, 44.800878813388955, 54.3436379411576, 38.793430087539925, 62.33032034173538, 52.145569050301965, 54.14055320752649, 51.77057420353263, 50.6348864829263, 48.13777367359697, 50.533729704562745, 48.191295966858526, 53.184262139651516, 55.688136378322405, 54.075316455203726, 51.5152605314269, 58.73184389781358, 59.824873717003534, 0.9235100780538894, 0.6731888387287681, 81.03407515241605, 1.0515865817246215, 0.7366848638814468, 87.74194843884946, 120.37642523668, 1.6009609739049502, 1.4783508750993735, 1.0777376222313648, 0.8504514467580526, 129.71561315487284, 10.456550543117054, 1.6835344359566062, 10.205560907744363, 1.1793672287091594, 1.8825200876362658, 1.738561802376501, 1.2672710082388736, 152.54815655558545, 1.9795228644889624, 1.3868639193766918, 77.04985853685946, 0.7005570649881765, 1.2535458840018565, 0.9138127481179621, 109.9923359701672, 20.002519662376, 8.866390937369047, 8.653768570133764, 0.9263890000000004, 0.6741977499999999, 81.0649000000001, 1.055178000000001, 0.7375619999999988, 87.51254999999998, 120.25062499999997, 1.6013082499999982, 1.483183000000004, 1.0795155000000005, 0.8539020000000004, 129.80012500000004, 10.457124750000002, 1.6895639999999994, 10.213064000000013, 1.1810464999999994, 1.87534775, 1.7370455000000014, 1.2642447500000011, 152.02124999999995, 1.9786157500000001, 1.3831722499999999, 76.82817500000017, 0.6990420000000007, 1.2558232500000013, 0.9140142499999998, 109.90775000000023, 19.993885000000027, 8.85424225000001, 8.6475515]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  -0.1448398774946711\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.1881078879038494  minutes\n",
            "======PPO Validation from:  20210602 to  20210811\n",
            "[165239.02218265695, 0.9203, 0.677975, 81.2695, 1.04799, 0.73502, 88.30799999999999, 119.8765, 1.595065, 1.46738, 1.0815350000000001, 0.8469, 129.58100000000002, 10.45575, 1.671555, 10.21675, 1.1719950000000001, 1.8837700000000002, 1.7337650000000002, 1.276735, 153.0975, 1.974145, 1.38435, 77.5015, 0.701105, 1.251965, 0.922375, 110.57, 20.08544, 8.92135, 8.7174, 0.0, 0.0, 208.06300735473633, 1526.498083114624, 171.9038908779621, 100.0, 533.06858253479, 95.41703844070435, 1017.9596252441406, 895.8546331524849, 0.0, 1618.1552391052246, 32.74818992614746, 0.0, 0.0, 539.7372708320618, 247.63899898529053, 0.0, 100.0, 946.5181982517242, 44.33967971801758, 639.9380594491959, 125.09162521362305, 1469.5072835683823, 1424.6207084655762, 1248.2217627763748, 3206.773321866989, 100.0, 1033.8257093429565, 42.381967544555664, -0.0026990765381580406, -0.003685586233816429, -0.45305846582324705, -0.005164934630627949, -0.003920975387673997, -0.23523107996511783, -0.013221999880542512, 0.0039546081126944355, -0.0006225435163658144, -0.0031393420826704155, -0.002472604001438672, -0.4004995533421152, 0.04564540283850249, -0.004003805512231784, 0.008756649602331024, -0.0032909856497294054, 0.010032300071677769, 0.0043634653932753675, -9.573422783182295e-06, -0.019329306217343856, 0.0010603545544483062, 0.0002116577525026475, -0.05638345395317401, -0.0002950524943947652, 0.0029134073179482023, -0.00014103342797933127, -0.03147981872477601, 0.007489282819598486, 0.06249632292141705, 0.03115897991554739, 41.700528622906404, 44.571046916077094, 43.934672840216614, 36.88683652257833, 41.42211684431859, 49.94454548377505, 47.71020092818331, 53.24614836292338, 45.18002222685545, 45.06957044257896, 41.608810524541234, 44.29898426852809, 55.7574118364109, 44.800878813388955, 54.3436379411576, 38.793430087539925, 62.33032034173538, 52.145569050301965, 54.14055320752649, 51.77057420353263, 50.6348864829263, 48.13777367359697, 50.533729704562745, 48.191295966858526, 53.184262139651516, 55.688136378322405, 54.075316455203726, 51.5152605314269, 58.73184389781358, 59.824873717003534, 0.9235100780538894, 0.6731888387287681, 81.03407515241605, 1.0515865817246215, 0.7366848638814468, 87.74194843884946, 120.37642523668, 1.6009609739049502, 1.4783508750993735, 1.0777376222313648, 0.8504514467580526, 129.71561315487284, 10.456550543117054, 1.6835344359566062, 10.205560907744363, 1.1793672287091594, 1.8825200876362658, 1.738561802376501, 1.2672710082388736, 152.54815655558545, 1.9795228644889624, 1.3868639193766918, 77.04985853685946, 0.7005570649881765, 1.2535458840018565, 0.9138127481179621, 109.9923359701672, 20.002519662376, 8.866390937369047, 8.653768570133764, 0.9263890000000004, 0.6741977499999999, 81.0649000000001, 1.055178000000001, 0.7375619999999988, 87.51254999999998, 120.25062499999997, 1.6013082499999982, 1.483183000000004, 1.0795155000000005, 0.8539020000000004, 129.80012500000004, 10.457124750000002, 1.6895639999999994, 10.213064000000013, 1.1810464999999994, 1.87534775, 1.7370455000000014, 1.2642447500000011, 152.02124999999995, 1.9786157500000001, 1.3831722499999999, 76.82817500000017, 0.6990420000000007, 1.2558232500000013, 0.9140142499999998, 109.90775000000023, 19.993885000000027, 8.85424225000001, 8.6475515]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  -0.10271931407198732\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.1661041339238483  minutes\n",
            "======DDPG Validation from:  20210602 to  20210811\n",
            "[0.042097553328081405, 0.9203, 0.677975, 81.2695, 1.04799, 0.73502, 88.30799999999999, 119.8765, 1.595065, 1.46738, 1.0815350000000001, 0.8469, 129.58100000000002, 10.45575, 1.671555, 10.21675, 1.1719950000000001, 1.8837700000000002, 1.7337650000000002, 1.276735, 153.0975, 1.974145, 1.38435, 77.5015, 0.701105, 1.251965, 0.922375, 110.57, 20.08544, 8.92135, 8.7174, 0, 0, 0.0, 1600.9993896484375, 0, 1599.9994583129883, 1599.8844985961914, 1596.987808227539, 0, 1363.7382736206055, 1599.9765396118164, 1599.9990310668945, 1669.999870300293, 0, 1548.1276321411133, 1599.9996795654297, 0, 1602.9999618530273, 1599.9925537109375, 1599.997459411621, 0, 0, 1598.1556701660156, 0, 0, 1699.9845886230469, 0.0, 0, 1599.8678741455078, 1599.9996490478516, -0.0026990765381580406, -0.003685586233816429, -0.45305846582324705, -0.005164934630627949, -0.003920975387673997, -0.23523107996511783, -0.013221999880542512, 0.0039546081126944355, -0.0006225435163658144, -0.0031393420826704155, -0.002472604001438672, -0.4004995533421152, 0.04564540283850249, -0.004003805512231784, 0.008756649602331024, -0.0032909856497294054, 0.010032300071677769, 0.0043634653932753675, -9.573422783182295e-06, -0.019329306217343856, 0.0010603545544483062, 0.0002116577525026475, -0.05638345395317401, -0.0002950524943947652, 0.0029134073179482023, -0.00014103342797933127, -0.03147981872477601, 0.007489282819598486, 0.06249632292141705, 0.03115897991554739, 41.700528622906404, 44.571046916077094, 43.934672840216614, 36.88683652257833, 41.42211684431859, 49.94454548377505, 47.71020092818331, 53.24614836292338, 45.18002222685545, 45.06957044257896, 41.608810524541234, 44.29898426852809, 55.7574118364109, 44.800878813388955, 54.3436379411576, 38.793430087539925, 62.33032034173538, 52.145569050301965, 54.14055320752649, 51.77057420353263, 50.6348864829263, 48.13777367359697, 50.533729704562745, 48.191295966858526, 53.184262139651516, 55.688136378322405, 54.075316455203726, 51.5152605314269, 58.73184389781358, 59.824873717003534, 0.9235100780538894, 0.6731888387287681, 81.03407515241605, 1.0515865817246215, 0.7366848638814468, 87.74194843884946, 120.37642523668, 1.6009609739049502, 1.4783508750993735, 1.0777376222313648, 0.8504514467580526, 129.71561315487284, 10.456550543117054, 1.6835344359566062, 10.205560907744363, 1.1793672287091594, 1.8825200876362658, 1.738561802376501, 1.2672710082388736, 152.54815655558545, 1.9795228644889624, 1.3868639193766918, 77.04985853685946, 0.7005570649881765, 1.2535458840018565, 0.9138127481179621, 109.9923359701672, 20.002519662376, 8.866390937369047, 8.653768570133764, 0.9263890000000004, 0.6741977499999999, 81.0649000000001, 1.055178000000001, 0.7375619999999988, 87.51254999999998, 120.25062499999997, 1.6013082499999982, 1.483183000000004, 1.0795155000000005, 0.8539020000000004, 129.80012500000004, 10.457124750000002, 1.6895639999999994, 10.213064000000013, 1.1810464999999994, 1.87534775, 1.7370455000000014, 1.2642447500000011, 152.02124999999995, 1.9786157500000001, 1.3831722499999999, 76.82817500000017, 0.6990420000000007, 1.2558232500000013, 0.9140142499999998, 109.90775000000023, 19.993885000000027, 8.85424225000001, 8.6475515]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 0        |\n",
            "| fps                     | 14       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | -0.759   |\n",
            "| reference_Q_mean        | 1.13     |\n",
            "| reference_Q_std         | 0.495    |\n",
            "| reference_action_mean   | 0.134    |\n",
            "| reference_action_std    | 0.978    |\n",
            "| reference_actor_Q_mean  | 1.23     |\n",
            "| reference_actor_Q_std   | 0.506    |\n",
            "| rollout/Q_mean          | 0.754    |\n",
            "| rollout/actions_mean    | -0.0377  |\n",
            "| rollout/actions_std     | 0.83     |\n",
            "| rollout/episode_steps   | 945      |\n",
            "| rollout/episodes        | 21       |\n",
            "| rollout/return          | -1.2     |\n",
            "| rollout/return_history  | -1.2     |\n",
            "| total/duration          | 129      |\n",
            "| total/episodes          | 21       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 155      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -1.38    |\n",
            "| train/loss_critic       | 0.106    |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.00704  |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.467   |\n",
            "| fps                | 235      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 42       |\n",
            "| policy_loss        | -0.429   |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 1.52     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.717   |\n",
            "| fps                | 254      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 42.1     |\n",
            "| policy_loss        | -0.862   |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 0.811    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.146   |\n",
            "| fps                | 259      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 42.1     |\n",
            "| policy_loss        | -1.59    |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 0.247    |\n",
            "---------------------------------\n",
            "----------------------------------\n",
            "| explained_variance | -1.19e-07 |\n",
            "| fps                | 263       |\n",
            "| nupdates           | 400       |\n",
            "| policy_entropy     | 42.2      |\n",
            "| policy_loss        | -2.23     |\n",
            "| total_timesteps    | 8000      |\n",
            "| value_loss         | 0.208     |\n",
            "----------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 5.96e-08 |\n",
            "| fps                | 264      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 42.2     |\n",
            "| policy_loss        | 0.72     |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 0.079    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0        |\n",
            "| fps                | 266      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 42.3     |\n",
            "| policy_loss        | 0.0302   |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 0.387    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 1.19e-07 |\n",
            "| fps                | 266      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 42.4     |\n",
            "| policy_loss        | 1.1      |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 0.227    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.00899  |\n",
            "| fps                | 267      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 42.4     |\n",
            "| policy_loss        | -0.907   |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 0.405    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 1.19e-07 |\n",
            "| fps                | 267      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 42.6     |\n",
            "| policy_loss        | -0.385   |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 1.02     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.072    |\n",
            "| fps                | 267      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 42.6     |\n",
            "| policy_loss        | 0.134    |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 0.47     |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.2944644172986348  minutes\n",
            "======ACKTR Validation from:  20210602 to  20210811\n",
            "[276550.507785674, 0.9203, 0.677975, 81.2695, 1.04799, 0.73502, 88.30799999999999, 119.8765, 1.595065, 1.46738, 1.0815350000000001, 0.8469, 129.58100000000002, 10.45575, 1.671555, 10.21675, 1.1719950000000001, 1.8837700000000002, 1.7337650000000002, 1.276735, 153.0975, 1.974145, 1.38435, 77.5015, 0.701105, 1.251965, 0.922375, 110.57, 20.08544, 8.92135, 8.7174, 3028.846096277237, 4066.1869196891785, 0.0, 4570.588310241699, 0, 1938.310269355774, 0.0, 0.0, 4740.790332794189, 979.0892286300659, 2036.9868822097778, 2371.385080575943, 4900.0, 0.0, 3977.018222808838, 112.95814514160156, 0.0, 4900.0, 4900.0, 0.0, 1654.0177111625671, 51.15222930908203, 1323.4558577537537, 4900.0, 3321.010290622711, 91.52311325073242, 0.0, 0.0, 0.0, 0.0, -0.0026990765381580406, -0.003685586233816429, -0.45305846582324705, -0.005164934630627949, -0.003920975387673997, -0.23523107996511783, -0.013221999880542512, 0.0039546081126944355, -0.0006225435163658144, -0.0031393420826704155, -0.002472604001438672, -0.4004995533421152, 0.04564540283850249, -0.004003805512231784, 0.008756649602331024, -0.0032909856497294054, 0.010032300071677769, 0.0043634653932753675, -9.573422783182295e-06, -0.019329306217343856, 0.0010603545544483062, 0.0002116577525026475, -0.05638345395317401, -0.0002950524943947652, 0.0029134073179482023, -0.00014103342797933127, -0.03147981872477601, 0.007489282819598486, 0.06249632292141705, 0.03115897991554739, 41.700528622906404, 44.571046916077094, 43.934672840216614, 36.88683652257833, 41.42211684431859, 49.94454548377505, 47.71020092818331, 53.24614836292338, 45.18002222685545, 45.06957044257896, 41.608810524541234, 44.29898426852809, 55.7574118364109, 44.800878813388955, 54.3436379411576, 38.793430087539925, 62.33032034173538, 52.145569050301965, 54.14055320752649, 51.77057420353263, 50.6348864829263, 48.13777367359697, 50.533729704562745, 48.191295966858526, 53.184262139651516, 55.688136378322405, 54.075316455203726, 51.5152605314269, 58.73184389781358, 59.824873717003534, 0.9235100780538894, 0.6731888387287681, 81.03407515241605, 1.0515865817246215, 0.7366848638814468, 87.74194843884946, 120.37642523668, 1.6009609739049502, 1.4783508750993735, 1.0777376222313648, 0.8504514467580526, 129.71561315487284, 10.456550543117054, 1.6835344359566062, 10.205560907744363, 1.1793672287091594, 1.8825200876362658, 1.738561802376501, 1.2672710082388736, 152.54815655558545, 1.9795228644889624, 1.3868639193766918, 77.04985853685946, 0.7005570649881765, 1.2535458840018565, 0.9138127481179621, 109.9923359701672, 20.002519662376, 8.866390937369047, 8.653768570133764, 0.9263890000000004, 0.6741977499999999, 81.0649000000001, 1.055178000000001, 0.7375619999999988, 87.51254999999998, 120.25062499999997, 1.6013082499999982, 1.483183000000004, 1.0795155000000005, 0.8539020000000004, 129.80012500000004, 10.457124750000002, 1.6895639999999994, 10.213064000000013, 1.1810464999999994, 1.87534775, 1.7370455000000014, 1.2642447500000011, 152.02124999999995, 1.9786157500000001, 1.3831722499999999, 76.82817500000017, 0.6990420000000007, 1.2558232500000013, 0.9140142499999998, 109.90775000000023, 19.993885000000027, 8.85424225000001, 8.6475515]\n",
            "adjcp:  30\n",
            "======Trading from:  20210811 to  20211018\n",
            "previous_total_asset:1039971.8532420355\n",
            "end_total_asset:1064301.1055272815\n",
            "total_reward:24329.25228524604\n",
            "total_cost:  117.166383025823\n",
            "total trades:  1099\n",
            "Sharpe:  0.29750047581246974\n",
            "previous_total_asset:1039971.8532420355\n",
            "end_total_asset:1061856.6316411037\n",
            "total_reward:21884.778399068164\n",
            "total_cost:  12.09425016349021\n",
            "total trades:  888\n",
            "Sharpe:  0.28013894415513874\n",
            "previous_total_asset:1039971.8532420355\n",
            "end_total_asset:1062521.9080410306\n",
            "total_reward:22550.054798995145\n",
            "total_cost:  1199.666150910027\n",
            "total trades:  1273\n",
            "Sharpe:  0.2771899856593637\n",
            "previous_total_asset:1039971.8532420355\n",
            "end_total_asset:1052596.5986438917\n",
            "total_reward:12624.745401856257\n",
            "total_cost:  2065.3558071372668\n",
            "total trades:  1211\n",
            "Sharpe:  0.17950063983729644\n",
            "previous_total_asset:1039971.8532420355\n",
            "end_total_asset:1062442.9396281478\n",
            "total_reward:22471.086386112263\n",
            "total_cost:  1763.1647481969837\n",
            "total trades:  1330\n",
            "Sharpe:  0.2867095065641774\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.9221950000000001, 0.67981, 81.4565, 1.04746, 0.73757, 88.3295, 119.78299999999999, 1.5915700000000002, 1.46777, 1.082435, 0.8467049999999999, 129.648, 10.39685, 1.667305, 10.1973, 1.1739, 1.880575, 1.7338099999999999, 1.278075, 153.19, 1.96908, 1.3865100000000001, 77.771, 0.7041649999999999, 1.25039, 0.9219950000000001, 110.4435, 19.92685, 8.85345, 8.685205, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.00263740845618754, -0.002902262983052406, -0.3882729616223628, -0.0051787345856662, -0.0035903560307589677, -0.17051827480558757, -0.060108037952630866, 0.0029188256245402666, -0.001467522373471919, -0.0025550950838251207, -0.0026321377542837254, -0.3797690908222364, 0.03782269021950313, -0.0051018265114848305, 0.007497405684382841, -0.0035275017315303803, 0.009238056221715585, 0.003697400006346996, 0.0008901241409291494, 0.03718709473062631, 0.0001345403634582798, 0.00018832767246457216, 0.007730979216802325, 2.9570578423010474e-05, 0.002452887173481688, 0.0005361164253553374, 0.007979162867457035, 0.0012558900469485934, 0.05743613311803486, 0.03172512364330338, 43.132416008626976, 46.15405708949281, 45.057998418707214, 36.57856415789313, 43.08327224025357, 50.03565140864668, 47.24344714413209, 51.76712389256937, 45.3405590342856, 46.022786129885596, 41.470548741519934, 44.670380441411965, 53.21957364813369, 43.71164817397361, 52.17307392520417, 40.20180293938352, 60.786163111618386, 52.161228440417155, 54.73652588871374, 52.08207796758525, 49.27791966483848, 48.98660100086563, 51.73997361819116, 49.79691044845795, 52.53110592048802, 55.43099772282934, 53.31954955001429, 48.38781285770296, 56.10343238412343, 57.62050531502156, 0.9232709729531824, 0.6743926862326285, 81.11087967015858, 1.0508362941383267, 0.7368457977211837, 87.84877599542229, 120.26852973910182, 1.5992535241040504, 1.4764270796267602, 1.0785916909165714, 0.8497702746202248, 129.70331985398687, 10.445695898913952, 1.6805836294190417, 10.204058924518115, 1.1783731871256757, 1.8821664353387628, 1.7376978383080461, 1.2692353703772603, 152.66485536366085, 1.9776241618546058, 1.3867995703991116, 77.18097516652138, 0.7012130531721443, 1.2529720869106098, 0.9153004302783325, 110.07436579377317, 19.988761541943997, 8.864038039665585, 8.659484284654898, 0.9256912500000002, 0.6739692499999999, 81.02537500000008, 1.0544070000000012, 0.7370414999999988, 87.53357499999997, 120.22727499999996, 1.601783499999998, 1.482519250000004, 1.0794835000000005, 0.8535405000000005, 129.77482500000002, 10.459949750000003, 1.6888352499999992, 10.212926500000014, 1.1805607499999995, 1.87671575, 1.7370550000000011, 1.2647730000000013, 152.05982499999996, 1.9785130000000002, 1.38319425, 76.84737500000017, 0.6990802500000007, 1.2557825000000014, 0.9143632499999997, 109.93200000000022, 19.996182500000028, 8.860127250000009, 8.65093175]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20210811\n",
            "======TD3 Training========\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 4        |\n",
            "| fps                     | 116      |\n",
            "| mean 100 episode reward | -1.6     |\n",
            "| n_updates               | 3800     |\n",
            "| qf1_loss                | 201229.4 |\n",
            "| qf2_loss                | 57767.8  |\n",
            "| time_elapsed            | 34       |\n",
            "| total timesteps         | 3980     |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 8        |\n",
            "| fps                     | 110      |\n",
            "| mean 100 episode reward | -1.8     |\n",
            "| n_updates               | 7800     |\n",
            "| qf1_loss                | 99048.21 |\n",
            "| qf2_loss                | 34694.28 |\n",
            "| time_elapsed            | 71       |\n",
            "| total timesteps         | 7960     |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 108       |\n",
            "| mean 100 episode reward | -1.7      |\n",
            "| n_updates               | 11800     |\n",
            "| qf1_loss                | 336188.56 |\n",
            "| qf2_loss                | 181176.73 |\n",
            "| time_elapsed            | 110       |\n",
            "| total timesteps         | 11940     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 16        |\n",
            "| fps                     | 106       |\n",
            "| mean 100 episode reward | -1.7      |\n",
            "| n_updates               | 15800     |\n",
            "| qf1_loss                | 49686.484 |\n",
            "| qf2_loss                | 44960.266 |\n",
            "| time_elapsed            | 149       |\n",
            "| total timesteps         | 15920     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 20        |\n",
            "| fps                     | 105       |\n",
            "| mean 100 episode reward | -1.7      |\n",
            "| n_updates               | 19800     |\n",
            "| qf1_loss                | 32875.65  |\n",
            "| qf2_loss                | 36835.086 |\n",
            "| time_elapsed            | 188       |\n",
            "| total timesteps         | 19900     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  3.1848324100176493  minutes\n",
            "======TD3 Validation from:  20210811 to  20211018\n",
            "[0.056910165035048976, 0.9174899999999999, 0.68504, 84.779, 1.04865, 0.74203, 92.426, 123.78200000000001, 1.56318, 1.433925, 1.0708, 0.8435450000000001, 132.5365, 9.76945, 1.63985, 9.98803, 1.1598899999999999, 1.8520150000000002, 1.699325, 1.269285, 157.096, 1.94335, 1.3750850000000001, 80.82300000000001, 0.7073550000000001, 1.23632, 0.9231299999999999, 114.2605, 20.33915, 8.41649, 8.61115, 0, 0, 1800.0, 0.0, 3600.0, 1800.0, 1900.0, 0, 1800.0, 3406.0, 1901.0, 1810.0, 1812.0, 0, 0, 1804.0, 0.0, 0, 3700.0, 0, 1800.0, 1800.0, 1800.0, 1805.0, 1800.0, 0, 0, 1800.0, 1800.0, 0, -0.002074824770630368, 0.0027798920574176877, 0.9506163389549585, 0.004283688342599401, 0.0023171135575403845, 1.2344145880902317, 0.9090324547184423, -0.011550680212947695, -0.013949533118428503, -0.003432461378502838, -0.0025104877782493595, 0.5620845197644257, -0.10883346830155638, -0.005280570813841479, -0.03401237587104866, -0.00479563278100259, -0.008181133913012673, -0.011382851982520092, -0.0003100027843176978, 1.1094033018850382, -0.00047749947296993334, -0.0016055764523434934, 0.586296615297158, -0.0006616281150739756, -0.00680409793599801, 0.0008327954753925004, 0.9396389542635148, 0.13284852015365445, -0.05838818699081649, 0.006327235978156764, 45.68229977959253, 55.59757497085054, 65.82890488388952, 50.467195236233884, 54.63361330888201, 69.16504722210334, 68.7277095732178, 38.169840552292435, 34.35233693279022, 41.22569663611984, 40.03757349070574, 61.991423802246885, 33.87043905218613, 39.91418842643895, 36.49989280150366, 41.22728242342432, 43.65529574145801, 39.96200284084656, 51.99379172747883, 65.21937760361203, 44.55708992010165, 51.21173760865525, 63.88714215119319, 53.782536817112195, 41.317678430589034, 50.76830730127509, 67.40720020975382, 52.0714755300891, 41.02014444840953, 47.18616185582213, 0.9172454739382297, 0.6807338430652123, 82.95216726298523, 1.0525805783333582, 0.7350192192101459, 90.44019526616727, 121.85027880806442, 1.5769958334037193, 1.4464755939156109, 1.073413185437004, 0.849437704291699, 130.7943890939716, 9.88363095482614, 1.6598665530717849, 10.088255107222738, 1.1590187546118458, 1.8562398794535584, 1.7027087689340261, 1.2638846662595484, 153.98124314603487, 1.953877663056746, 1.364469882375719, 78.80635651610507, 0.6982873277761854, 1.247977966218424, 0.92611950534664, 112.84876214719844, 20.543493711060577, 8.526663260489821, 8.704217729483371, 0.91828875, 0.676, 81.4445250000001, 1.0462970000000007, 0.729290749999999, 88.69374999999998, 120.46194999999997, 1.5943037499999981, 1.4640772500000039, 1.0776437500000007, 0.8538252499999999, 129.81982500000007, 9.98964175, 1.6680579999999998, 10.135896500000014, 1.1625910000000002, 1.8671477499999996, 1.7146465000000002, 1.2623917500000015, 152.05234999999993, 1.9534607500000007, 1.3616559999999998, 77.83617500000021, 0.6970122500000004, 1.259256250000001, 0.9269522499999999, 111.66690000000028, 20.426378000000028, 8.592121500000008, 8.718427500000002]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  0.477790578493044\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.2801880518595377  minutes\n",
            "======PPO Validation from:  20210811 to  20211018\n",
            "[788777.2136287165, 0.9174899999999999, 0.68504, 84.779, 1.04865, 0.74203, 92.426, 123.78200000000001, 1.56318, 1.433925, 1.0708, 0.8435450000000001, 132.5365, 9.76945, 1.63985, 9.98803, 1.1598899999999999, 1.8520150000000002, 1.699325, 1.269285, 157.096, 1.94335, 1.3750850000000001, 80.82300000000001, 0.7073550000000001, 1.23632, 0.9231299999999999, 114.2605, 20.33915, 8.41649, 8.61115, 3053.5721168518066, 413.76877212524414, 631.0632319450378, 583.6935548782349, 465.0218086242676, 431.5666438639164, 0.0, 265.447717666626, 38.97911834716797, 2118.6242713928223, 0.0, 47.281288146972656, 860.3458042144775, 1826.0723624229431, 185.40930557250977, 162.3738071322441, 1150.3621176481247, 146.22340142726898, 1442.449783563614, 87.05872344970703, 2204.4977729320526, 127.5122299194336, 452.1564647555351, 572.9602708816528, 1892.6056280136108, 213.27028691768646, 91.8916015625, 20.288357257843018, 282.3473403453827, 2446.7657995224, -0.002074824770630368, 0.0027798920574176877, 0.9506163389549585, 0.004283688342599401, 0.0023171135575403845, 1.2344145880902317, 0.9090324547184423, -0.011550680212947695, -0.013949533118428503, -0.003432461378502838, -0.0025104877782493595, 0.5620845197644257, -0.10883346830155638, -0.005280570813841479, -0.03401237587104866, -0.00479563278100259, -0.008181133913012673, -0.011382851982520092, -0.0003100027843176978, 1.1094033018850382, -0.00047749947296993334, -0.0016055764523434934, 0.586296615297158, -0.0006616281150739756, -0.00680409793599801, 0.0008327954753925004, 0.9396389542635148, 0.13284852015365445, -0.05838818699081649, 0.006327235978156764, 45.68229977959253, 55.59757497085054, 65.82890488388952, 50.467195236233884, 54.63361330888201, 69.16504722210334, 68.7277095732178, 38.169840552292435, 34.35233693279022, 41.22569663611984, 40.03757349070574, 61.991423802246885, 33.87043905218613, 39.91418842643895, 36.49989280150366, 41.22728242342432, 43.65529574145801, 39.96200284084656, 51.99379172747883, 65.21937760361203, 44.55708992010165, 51.21173760865525, 63.88714215119319, 53.782536817112195, 41.317678430589034, 50.76830730127509, 67.40720020975382, 52.0714755300891, 41.02014444840953, 47.18616185582213, 0.9172454739382297, 0.6807338430652123, 82.95216726298523, 1.0525805783333582, 0.7350192192101459, 90.44019526616727, 121.85027880806442, 1.5769958334037193, 1.4464755939156109, 1.073413185437004, 0.849437704291699, 130.7943890939716, 9.88363095482614, 1.6598665530717849, 10.088255107222738, 1.1590187546118458, 1.8562398794535584, 1.7027087689340261, 1.2638846662595484, 153.98124314603487, 1.953877663056746, 1.364469882375719, 78.80635651610507, 0.6982873277761854, 1.247977966218424, 0.92611950534664, 112.84876214719844, 20.543493711060577, 8.526663260489821, 8.704217729483371, 0.91828875, 0.676, 81.4445250000001, 1.0462970000000007, 0.729290749999999, 88.69374999999998, 120.46194999999997, 1.5943037499999981, 1.4640772500000039, 1.0776437500000007, 0.8538252499999999, 129.81982500000007, 9.98964175, 1.6680579999999998, 10.135896500000014, 1.1625910000000002, 1.8671477499999996, 1.7146465000000002, 1.2623917500000015, 152.05234999999993, 1.9534607500000007, 1.3616559999999998, 77.83617500000021, 0.6970122500000004, 1.259256250000001, 0.9269522499999999, 111.66690000000028, 20.426378000000028, 8.592121500000008, 8.718427500000002]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  0.36032162991330835\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.153778882821401  minutes\n",
            "======DDPG Validation from:  20210811 to  20211018\n",
            "[0.6355623839489951, 0.9174899999999999, 0.68504, 84.779, 1.04865, 0.74203, 92.426, 123.78200000000001, 1.56318, 1.433925, 1.0708, 0.8435450000000001, 132.5365, 9.76945, 1.63985, 9.98803, 1.1598899999999999, 1.8520150000000002, 1.699325, 1.269285, 157.096, 1.94335, 1.3750850000000001, 80.82300000000001, 0.7073550000000001, 1.23632, 0.9231299999999999, 114.2605, 20.33915, 8.41649, 8.61115, 1462.2762031555176, 2299.9082946777344, 2851.0, 0, 3010.0, 2299.951026916504, 0.0, 3357.0, 0, 2310.0, 2300.9451217651367, 0.0, 2677.9999771118164, 0, 2299.989677429199, 2299.985008239746, 2627.0, 2717.9999771118164, 0.0, 1980.4809265136719, 0.0, 0, 2220.191102027893, 0, 0, 0, 0.0, 0.0, 2299.981559753418, 0, -0.002074824770630368, 0.0027798920574176877, 0.9506163389549585, 0.004283688342599401, 0.0023171135575403845, 1.2344145880902317, 0.9090324547184423, -0.011550680212947695, -0.013949533118428503, -0.003432461378502838, -0.0025104877782493595, 0.5620845197644257, -0.10883346830155638, -0.005280570813841479, -0.03401237587104866, -0.00479563278100259, -0.008181133913012673, -0.011382851982520092, -0.0003100027843176978, 1.1094033018850382, -0.00047749947296993334, -0.0016055764523434934, 0.586296615297158, -0.0006616281150739756, -0.00680409793599801, 0.0008327954753925004, 0.9396389542635148, 0.13284852015365445, -0.05838818699081649, 0.006327235978156764, 45.68229977959253, 55.59757497085054, 65.82890488388952, 50.467195236233884, 54.63361330888201, 69.16504722210334, 68.7277095732178, 38.169840552292435, 34.35233693279022, 41.22569663611984, 40.03757349070574, 61.991423802246885, 33.87043905218613, 39.91418842643895, 36.49989280150366, 41.22728242342432, 43.65529574145801, 39.96200284084656, 51.99379172747883, 65.21937760361203, 44.55708992010165, 51.21173760865525, 63.88714215119319, 53.782536817112195, 41.317678430589034, 50.76830730127509, 67.40720020975382, 52.0714755300891, 41.02014444840953, 47.18616185582213, 0.9172454739382297, 0.6807338430652123, 82.95216726298523, 1.0525805783333582, 0.7350192192101459, 90.44019526616727, 121.85027880806442, 1.5769958334037193, 1.4464755939156109, 1.073413185437004, 0.849437704291699, 130.7943890939716, 9.88363095482614, 1.6598665530717849, 10.088255107222738, 1.1590187546118458, 1.8562398794535584, 1.7027087689340261, 1.2638846662595484, 153.98124314603487, 1.953877663056746, 1.364469882375719, 78.80635651610507, 0.6982873277761854, 1.247977966218424, 0.92611950534664, 112.84876214719844, 20.543493711060577, 8.526663260489821, 8.704217729483371, 0.91828875, 0.676, 81.4445250000001, 1.0462970000000007, 0.729290749999999, 88.69374999999998, 120.46194999999997, 1.5943037499999981, 1.4640772500000039, 1.0776437500000007, 0.8538252499999999, 129.81982500000007, 9.98964175, 1.6680579999999998, 10.135896500000014, 1.1625910000000002, 1.8671477499999996, 1.7146465000000002, 1.2623917500000015, 152.05234999999993, 1.9534607500000007, 1.3616559999999998, 77.83617500000021, 0.6970122500000004, 1.259256250000001, 0.9269522499999999, 111.66690000000028, 20.426378000000028, 8.592121500000008, 8.718427500000002]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 1.19e-07 |\n",
            "| fps                     | 14       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | 0.823    |\n",
            "| reference_Q_mean        | 1.28     |\n",
            "| reference_Q_std         | 0.809    |\n",
            "| reference_action_mean   | 0.206    |\n",
            "| reference_action_std    | 0.961    |\n",
            "| reference_actor_Q_mean  | 1.47     |\n",
            "| reference_actor_Q_std   | 0.959    |\n",
            "| rollout/Q_mean          | 0.429    |\n",
            "| rollout/actions_mean    | 0.0988   |\n",
            "| rollout/actions_std     | 0.816    |\n",
            "| rollout/episode_steps   | 995      |\n",
            "| rollout/episodes        | 20       |\n",
            "| rollout/return          | -1.57    |\n",
            "| rollout/return_history  | -1.57    |\n",
            "| total/duration          | 128      |\n",
            "| total/episodes          | 20       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 156      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -1.16    |\n",
            "| train/loss_critic       | 0.121    |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.00456  |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.675    |\n",
            "| fps                | 228      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | 0.383    |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 0.375    |\n",
            "---------------------------------\n",
            "----------------------------------\n",
            "| explained_variance | -2.38e-07 |\n",
            "| fps                | 246       |\n",
            "| nupdates           | 200       |\n",
            "| policy_entropy     | 43.3      |\n",
            "| policy_loss        | 0.336     |\n",
            "| total_timesteps    | 4000      |\n",
            "| value_loss         | 0.0201    |\n",
            "----------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 1.19e-07 |\n",
            "| fps                | 252      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 43.3     |\n",
            "| policy_loss        | -0.159   |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 0.872    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.451    |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | -0.872   |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.657    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.413    |\n",
            "| fps                | 256      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | -2.32    |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 0.428    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.196   |\n",
            "| fps                | 256      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | 0.0907   |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 0.392    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0728   |\n",
            "| fps                | 256      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 43.5     |\n",
            "| policy_loss        | -0.294   |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 0.194    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.291   |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 43.6     |\n",
            "| policy_loss        | 0.403    |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 3.79     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.025   |\n",
            "| fps                | 253      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 43.6     |\n",
            "| policy_loss        | -1.43    |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 0.498    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.06    |\n",
            "| fps                | 251      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 43.7     |\n",
            "| policy_loss        | -0.638   |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 1.73     |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.3765585621198018  minutes\n",
            "======ACKTR Validation from:  20210811 to  20211018\n",
            "[125879.14784778038, 0.9174899999999999, 0.68504, 84.779, 1.04865, 0.74203, 92.426, 123.78200000000001, 1.56318, 1.433925, 1.0708, 0.8435450000000001, 132.5365, 9.76945, 1.63985, 9.98803, 1.1598899999999999, 1.8520150000000002, 1.699325, 1.269285, 157.096, 1.94335, 1.3750850000000001, 80.82300000000001, 0.7073550000000001, 1.23632, 0.9231299999999999, 114.2605, 20.33915, 8.41649, 8.61115, 4728.748699188232, 35.71099090576172, 0.0, 4.031161308288574, 0.0, 0.0, 1903.965570449829, 75.51785087585449, 2834.2232749462128, 71.13331604003906, 1738.4654664993286, 0.0, 2413.599808692932, 0.0, 0.0, 1102.541205406189, 0.0, 126.61078643798828, 1190.7874941825867, 1352.4354810714722, 0.0, 1071.7088284492493, 4360.774435043335, 0.0, 0.0, 131.51873397827148, 0.0, 2799.394011735916, 0.0, 568.7705220580101, -0.002074824770630368, 0.0027798920574176877, 0.9506163389549585, 0.004283688342599401, 0.0023171135575403845, 1.2344145880902317, 0.9090324547184423, -0.011550680212947695, -0.013949533118428503, -0.003432461378502838, -0.0025104877782493595, 0.5620845197644257, -0.10883346830155638, -0.005280570813841479, -0.03401237587104866, -0.00479563278100259, -0.008181133913012673, -0.011382851982520092, -0.0003100027843176978, 1.1094033018850382, -0.00047749947296993334, -0.0016055764523434934, 0.586296615297158, -0.0006616281150739756, -0.00680409793599801, 0.0008327954753925004, 0.9396389542635148, 0.13284852015365445, -0.05838818699081649, 0.006327235978156764, 45.68229977959253, 55.59757497085054, 65.82890488388952, 50.467195236233884, 54.63361330888201, 69.16504722210334, 68.7277095732178, 38.169840552292435, 34.35233693279022, 41.22569663611984, 40.03757349070574, 61.991423802246885, 33.87043905218613, 39.91418842643895, 36.49989280150366, 41.22728242342432, 43.65529574145801, 39.96200284084656, 51.99379172747883, 65.21937760361203, 44.55708992010165, 51.21173760865525, 63.88714215119319, 53.782536817112195, 41.317678430589034, 50.76830730127509, 67.40720020975382, 52.0714755300891, 41.02014444840953, 47.18616185582213, 0.9172454739382297, 0.6807338430652123, 82.95216726298523, 1.0525805783333582, 0.7350192192101459, 90.44019526616727, 121.85027880806442, 1.5769958334037193, 1.4464755939156109, 1.073413185437004, 0.849437704291699, 130.7943890939716, 9.88363095482614, 1.6598665530717849, 10.088255107222738, 1.1590187546118458, 1.8562398794535584, 1.7027087689340261, 1.2638846662595484, 153.98124314603487, 1.953877663056746, 1.364469882375719, 78.80635651610507, 0.6982873277761854, 1.247977966218424, 0.92611950534664, 112.84876214719844, 20.543493711060577, 8.526663260489821, 8.704217729483371, 0.91828875, 0.676, 81.4445250000001, 1.0462970000000007, 0.729290749999999, 88.69374999999998, 120.46194999999997, 1.5943037499999981, 1.4640772500000039, 1.0776437500000007, 0.8538252499999999, 129.81982500000007, 9.98964175, 1.6680579999999998, 10.135896500000014, 1.1625910000000002, 1.8671477499999996, 1.7146465000000002, 1.2623917500000015, 152.05234999999993, 1.9534607500000007, 1.3616559999999998, 77.83617500000021, 0.6970122500000004, 1.259256250000001, 0.9269522499999999, 111.66690000000028, 20.426378000000028, 8.592121500000008, 8.718427500000002]\n",
            "adjcp:  30\n",
            "======Trading from:  20211018 to  20211223\n",
            "previous_total_asset:1062442.9396281478\n",
            "end_total_asset:1044444.3138934898\n",
            "total_reward:-17998.62573465798\n",
            "total_cost:  1563.0783299167613\n",
            "total trades:  1186\n",
            "Sharpe:  -0.22023541279297226\n",
            "previous_total_asset:1062442.9396281478\n",
            "end_total_asset:1040781.056395822\n",
            "total_reward:-21661.88323232578\n",
            "total_cost:  733.2846212418023\n",
            "total trades:  1082\n",
            "Sharpe:  -0.19945034588517732\n",
            "previous_total_asset:1062442.9396281478\n",
            "end_total_asset:1043024.2873124826\n",
            "total_reward:-19418.652315665153\n",
            "total_cost:  2296.0487943844114\n",
            "total trades:  1360\n",
            "Sharpe:  -0.26373512532714594\n",
            "previous_total_asset:1062442.9396281478\n",
            "end_total_asset:1043181.9948963516\n",
            "total_reward:-19260.94473179616\n",
            "total_cost:  1397.549296157752\n",
            "total trades:  1241\n",
            "Sharpe:  -0.2083467628122346\n",
            "previous_total_asset:1062442.9396281478\n",
            "end_total_asset:1044444.3138934898\n",
            "total_reward:-17998.62573465798\n",
            "total_cost:  1563.0783299167613\n",
            "total trades:  1186\n",
            "Sharpe:  -0.22023541279297226\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.9174399999999999, 0.68466, 84.7255, 1.0459049999999999, 0.74112, 92.348, 123.74700000000001, 1.566545, 1.43726, 1.07181, 0.845735, 132.7305, 9.777105, 1.6387200000000002, 10.0466, 1.1610200000000002, 1.8523, 1.69938, 1.267915, 156.9275, 1.937585, 1.372595, 81.007, 0.7085899999999999, 1.237895, 0.923745, 114.3215, 20.41164, 8.42181, 8.65315, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0019257441177087387, 0.0029437149776853078, 1.041795492478883, 0.0035038407005876238, 0.002701843849835295, 1.319075037787897, 1.0126918806446241, -0.011746102512425693, -0.013884005117090314, -0.003364891514532564, -0.00267288712732372, 0.6889566541840111, -0.11084292510689941, -0.00669283994023484, -0.03554099628396301, -0.004329568069777023, -0.008031854510923253, -0.010973470857913226, 4.411798191616967e-05, 1.2882600124860346, -0.0017580075901926229, -0.0008111865447244515, 0.7323804273092662, 0.0002287768524585898, -0.007251426594570898, 0.000569903638371505, 1.0042775354514504, 0.11376969185655383, -0.06343076078910492, 0.001524361788723283, 45.647648861591165, 55.30645612247366, 65.50049654058306, 48.43881894080179, 54.04490278715433, 68.68128943849618, 68.47460010874548, 39.605483280552775, 36.01913667490509, 42.25471881878826, 42.34695318814313, 62.661488850117735, 34.31317941426745, 39.64337029417671, 41.95083093240091, 42.15385484606651, 43.777496863303774, 39.98774856184782, 51.333252008860654, 64.5582313011093, 42.744305148239576, 50.237403119060325, 64.46308391646198, 54.41581470478029, 42.06931324940814, 51.17715804655129, 67.60952215994305, 53.44247211831681, 41.26434961740072, 49.40085078454525, 0.9172808423130971, 0.6814476897806283, 83.27459139698792, 1.0513668368182019, 0.7361284520810286, 90.78706885413685, 122.19513720659816, 1.5750956818757704, 1.4448000313854998, 1.0731216971757305, 0.8487644853295718, 131.14640925870404, 9.864262599403206, 1.6560217252405511, 10.080681451364057, 1.1593826174096922, 1.8555235377347297, 1.7021035382187488, 1.2646174542123578, 154.51692621039217, 1.9509153606827923, 1.3659471764892248, 79.20647351317687, 0.700160540907788, 1.2461446996332561, 0.9256877771017964, 113.11653266588962, 20.519520309049565, 8.507599031309853, 8.69493268775912, 0.917826, 0.6768382500000001, 81.7313750000001, 1.0469775000000008, 0.730189499999999, 89.04969999999997, 120.73594999999997, 1.591554999999998, 1.4608085000000037, 1.0771155000000008, 0.85319375, 130.05272500000007, 9.969224500000001, 1.6663059999999998, 10.128681500000015, 1.162014, 1.8653102499999996, 1.71208625, 1.2627032500000015, 152.43879999999993, 1.9528472500000007, 1.3619932499999998, 78.0607250000002, 0.6974157500000004, 1.2570730000000012, 0.9269617499999999, 111.92182500000028, 20.440295500000026, 8.578859000000007, 8.716513250000002]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20211018\n",
            "======TD3 Training========\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 4        |\n",
            "| fps                     | 107      |\n",
            "| mean 100 episode reward | 3.9      |\n",
            "| n_updates               | 4000     |\n",
            "| qf1_loss                | 9249.949 |\n",
            "| qf2_loss                | 15310.93 |\n",
            "| time_elapsed            | 38       |\n",
            "| total timesteps         | 4180     |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 8         |\n",
            "| fps                     | 108       |\n",
            "| mean 100 episode reward | 3.9       |\n",
            "| n_updates               | 8200      |\n",
            "| qf1_loss                | 11618.617 |\n",
            "| qf2_loss                | 17543.352 |\n",
            "| time_elapsed            | 77        |\n",
            "| total timesteps         | 8360      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 109       |\n",
            "| mean 100 episode reward | 3.8       |\n",
            "| n_updates               | 12400     |\n",
            "| qf1_loss                | 6551.135  |\n",
            "| qf2_loss                | 8078.7583 |\n",
            "| time_elapsed            | 114       |\n",
            "| total timesteps         | 12540     |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| current_lr              | 0.0003   |\n",
            "| episodes                | 16       |\n",
            "| fps                     | 109      |\n",
            "| mean 100 episode reward | 3.6      |\n",
            "| n_updates               | 16600    |\n",
            "| qf1_loss                | 9577.129 |\n",
            "| qf2_loss                | 9959.145 |\n",
            "| time_elapsed            | 153      |\n",
            "| total timesteps         | 16720    |\n",
            "--------------------------------------\n",
            "Training time (TD3):  3.0657917777697246  minutes\n",
            "======TD3 Validation from:  20211018 to  20211223\n",
            "[0.40157768529019244, 0.925955, 0.663375, 82.304, 1.059375, 0.72137, 88.8915, 124.0945, 1.5700450000000001, 1.453855, 1.04185, 0.8483799999999999, 129.2255, 10.059125, 1.6631349999999998, 10.284885000000001, 1.132685, 1.8506900000000002, 1.71367, 1.227425, 152.329, 1.960205, 1.3349600000000001, 77.708, 0.681045, 1.283555, 0.9199, 114.0985, 20.7163, 8.88048, 9.080925, 1800.0, 1801.0, 0, 0, 0, 0, 1800.0, 1801.0, 0, 0, 0, 1800.0, 0, 0, 0, 1807.0, 0, 1800.0, 1800.0, 1800.0, 1257.0, 0.0, 0, 1807.0, 1800.0, 0.0, 1790.0, 1700.0, 0, 0, 0.001946629542349032, -0.002647948741719919, -0.25715064850497527, 0.004321646595687678, -0.0025211927116839394, -0.46642732598988346, 0.10491257825249534, 0.0015947309360375694, 0.004552617244518498, -0.0030949673984690396, 0.0006857486830068593, -0.26432148625423224, 0.02502599860692456, 0.00841670795067806, 0.045232640546204905, -0.002752194733127711, 0.0004371683521449121, 0.0040155461970030615, -0.00469888726230594, -0.4345347803499635, 0.008382229403729902, -0.004372002960830068, -0.5653042097046352, -0.005211899086457472, 0.007105641950504227, -0.00043667002905223296, 0.04430361019689144, -0.03348506542280916, 0.04370111305634339, 0.061395905261301564, 56.424307834683866, 47.27709595900803, 50.878519466553115, 58.147416969699776, 48.44287602467505, 47.72528467966952, 56.71309605588089, 48.056150134133986, 52.511430970698804, 39.79077118617295, 48.67832040680166, 49.29524184178347, 49.19627143265391, 53.10039947861868, 56.05512864913029, 45.19001504613836, 48.88472675882898, 54.22355740275549, 45.22647077397043, 50.3750979353672, 55.275391498707776, 46.415795270059924, 47.3440889993862, 44.013093470841184, 56.64931285284355, 48.53747138730294, 54.45340865020412, 48.90819935216643, 51.587914943554004, 57.060880192866, 0.9183515432331117, 0.659697333115836, 81.40681303317265, 1.0556333646469587, 0.7153490146141207, 88.64996993624817, 123.40045449089153, 1.5788030381017688, 1.4497503077084477, 1.041359907781153, 0.8509285900244317, 128.52393248780388, 10.147705307538715, 1.6665290555408616, 10.276192747998511, 1.129384813036538, 1.8553423920100223, 1.703859196072411, 1.2236880164853305, 151.04883250240636, 1.958582719406751, 1.327185195776158, 77.12182678850793, 0.677704473351549, 1.2837664341972161, 0.9222587659800043, 113.79829201385753, 20.898145163840955, 8.985997114187152, 9.099865163762678, 0.9120965, 0.6580705000000001, 81.0287000000001, 1.0506240000000013, 0.7131484999999992, 88.83859999999999, 123.131725, 1.5838057499999987, 1.4443487500000038, 1.0420802500000006, 0.8511327500000002, 128.3203000000001, 10.192025, 1.6639569999999995, 10.26839600000001, 1.1293909999999998, 1.860612499999999, 1.6970907500000005, 1.2243067500000007, 150.77634999999995, 1.9549235, 1.3269707499999996, 77.12492500000019, 0.6788175000000005, 1.2789092500000014, 0.9228044999999996, 113.60902500000024, 21.146722250000032, 9.023625500000009, 9.092276999999997]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  -0.1578169756503129\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.2935667196909586  minutes\n",
            "======PPO Validation from:  20211018 to  20211223\n",
            "[196644.04113000128, 0.925955, 0.663375, 82.304, 1.059375, 0.72137, 88.8915, 124.0945, 1.5700450000000001, 1.453855, 1.04185, 0.8483799999999999, 129.2255, 10.059125, 1.6631349999999998, 10.284885000000001, 1.132685, 1.8506900000000002, 1.71367, 1.227425, 152.329, 1.960205, 1.3349600000000001, 77.708, 0.681045, 1.283555, 0.9199, 114.0985, 20.7163, 8.88048, 9.080925, 6.0572428703308105, 1520.8287267684937, 1826.1581835746765, 0.0, 499.93061351776123, 962.9820665717125, 722.6511387825012, 157.15969371795654, 1171.1362104415894, 351.0149273276329, 1425.4475811719894, 23.01364654302597, 1540.9560928344727, 656.9349222183228, 171.68904662132263, 902.1004056930542, 786.9415726661682, 33.483009338378906, 0.0, 2070.057558298111, 142.37922650575638, 712.4368948936462, 203.80808001756668, 3505.052022457123, 1426.6380560398102, 0.0, 707.6997718811035, 1041.701143682003, 27.65867328643799, 0.0, 0.001946629542349032, -0.002647948741719919, -0.25715064850497527, 0.004321646595687678, -0.0025211927116839394, -0.46642732598988346, 0.10491257825249534, 0.0015947309360375694, 0.004552617244518498, -0.0030949673984690396, 0.0006857486830068593, -0.26432148625423224, 0.02502599860692456, 0.00841670795067806, 0.045232640546204905, -0.002752194733127711, 0.0004371683521449121, 0.0040155461970030615, -0.00469888726230594, -0.4345347803499635, 0.008382229403729902, -0.004372002960830068, -0.5653042097046352, -0.005211899086457472, 0.007105641950504227, -0.00043667002905223296, 0.04430361019689144, -0.03348506542280916, 0.04370111305634339, 0.061395905261301564, 56.424307834683866, 47.27709595900803, 50.878519466553115, 58.147416969699776, 48.44287602467505, 47.72528467966952, 56.71309605588089, 48.056150134133986, 52.511430970698804, 39.79077118617295, 48.67832040680166, 49.29524184178347, 49.19627143265391, 53.10039947861868, 56.05512864913029, 45.19001504613836, 48.88472675882898, 54.22355740275549, 45.22647077397043, 50.3750979353672, 55.275391498707776, 46.415795270059924, 47.3440889993862, 44.013093470841184, 56.64931285284355, 48.53747138730294, 54.45340865020412, 48.90819935216643, 51.587914943554004, 57.060880192866, 0.9183515432331117, 0.659697333115836, 81.40681303317265, 1.0556333646469587, 0.7153490146141207, 88.64996993624817, 123.40045449089153, 1.5788030381017688, 1.4497503077084477, 1.041359907781153, 0.8509285900244317, 128.52393248780388, 10.147705307538715, 1.6665290555408616, 10.276192747998511, 1.129384813036538, 1.8553423920100223, 1.703859196072411, 1.2236880164853305, 151.04883250240636, 1.958582719406751, 1.327185195776158, 77.12182678850793, 0.677704473351549, 1.2837664341972161, 0.9222587659800043, 113.79829201385753, 20.898145163840955, 8.985997114187152, 9.099865163762678, 0.9120965, 0.6580705000000001, 81.0287000000001, 1.0506240000000013, 0.7131484999999992, 88.83859999999999, 123.131725, 1.5838057499999987, 1.4443487500000038, 1.0420802500000006, 0.8511327500000002, 128.3203000000001, 10.192025, 1.6639569999999995, 10.26839600000001, 1.1293909999999998, 1.860612499999999, 1.6970907500000005, 1.2243067500000007, 150.77634999999995, 1.9549235, 1.3269707499999996, 77.12492500000019, 0.6788175000000005, 1.2789092500000014, 0.9228044999999996, 113.60902500000024, 21.146722250000032, 9.023625500000009, 9.092276999999997]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  -0.16342091405490225\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.209432029724121  minutes\n",
            "======DDPG Validation from:  20211018 to  20211223\n",
            "[0.01261342197196702, 0.925955, 0.663375, 82.304, 1.059375, 0.72137, 88.8915, 124.0945, 1.5700450000000001, 1.453855, 1.04185, 0.8483799999999999, 129.2255, 10.059125, 1.6631349999999998, 10.284885000000001, 1.132685, 1.8506900000000002, 1.71367, 1.227425, 152.329, 1.960205, 1.3349600000000001, 77.708, 0.681045, 1.283555, 0.9199, 114.0985, 20.7163, 8.88048, 9.080925, 0.0, 2199.99991607666, 0, 2200.0, 2201.0, 2200.0, 2200.0, 0, 0, 2199.999610900879, 0, 0, 2207.0, 0, 0, 2201.9993209838867, 2199.9822845458984, 0, 2200.0, 0, 0, 0, 2199.991012573242, 2200.0, 2200.0, 0, 2099.2979278564453, 2199.999755859375, 2199.999771118164, 0, 0.001946629542349032, -0.002647948741719919, -0.25715064850497527, 0.004321646595687678, -0.0025211927116839394, -0.46642732598988346, 0.10491257825249534, 0.0015947309360375694, 0.004552617244518498, -0.0030949673984690396, 0.0006857486830068593, -0.26432148625423224, 0.02502599860692456, 0.00841670795067806, 0.045232640546204905, -0.002752194733127711, 0.0004371683521449121, 0.0040155461970030615, -0.00469888726230594, -0.4345347803499635, 0.008382229403729902, -0.004372002960830068, -0.5653042097046352, -0.005211899086457472, 0.007105641950504227, -0.00043667002905223296, 0.04430361019689144, -0.03348506542280916, 0.04370111305634339, 0.061395905261301564, 56.424307834683866, 47.27709595900803, 50.878519466553115, 58.147416969699776, 48.44287602467505, 47.72528467966952, 56.71309605588089, 48.056150134133986, 52.511430970698804, 39.79077118617295, 48.67832040680166, 49.29524184178347, 49.19627143265391, 53.10039947861868, 56.05512864913029, 45.19001504613836, 48.88472675882898, 54.22355740275549, 45.22647077397043, 50.3750979353672, 55.275391498707776, 46.415795270059924, 47.3440889993862, 44.013093470841184, 56.64931285284355, 48.53747138730294, 54.45340865020412, 48.90819935216643, 51.587914943554004, 57.060880192866, 0.9183515432331117, 0.659697333115836, 81.40681303317265, 1.0556333646469587, 0.7153490146141207, 88.64996993624817, 123.40045449089153, 1.5788030381017688, 1.4497503077084477, 1.041359907781153, 0.8509285900244317, 128.52393248780388, 10.147705307538715, 1.6665290555408616, 10.276192747998511, 1.129384813036538, 1.8553423920100223, 1.703859196072411, 1.2236880164853305, 151.04883250240636, 1.958582719406751, 1.327185195776158, 77.12182678850793, 0.677704473351549, 1.2837664341972161, 0.9222587659800043, 113.79829201385753, 20.898145163840955, 8.985997114187152, 9.099865163762678, 0.9120965, 0.6580705000000001, 81.0287000000001, 1.0506240000000013, 0.7131484999999992, 88.83859999999999, 123.131725, 1.5838057499999987, 1.4443487500000038, 1.0420802500000006, 0.8511327500000002, 128.3203000000001, 10.192025, 1.6639569999999995, 10.26839600000001, 1.1293909999999998, 1.860612499999999, 1.6970907500000005, 1.2243067500000007, 150.77634999999995, 1.9549235, 1.3269707499999996, 77.12492500000019, 0.6788175000000005, 1.2789092500000014, 0.9228044999999996, 113.60902500000024, 21.146722250000032, 9.023625500000009, 9.092276999999997]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 0        |\n",
            "| fps                     | 14       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | 0.0372   |\n",
            "| reference_Q_mean        | 1.89     |\n",
            "| reference_Q_std         | 0.586    |\n",
            "| reference_action_mean   | 0.133    |\n",
            "| reference_action_std    | 0.978    |\n",
            "| reference_actor_Q_mean  | 2.16     |\n",
            "| reference_actor_Q_std   | 0.648    |\n",
            "| rollout/Q_mean          | 0.919    |\n",
            "| rollout/actions_mean    | 0.142    |\n",
            "| rollout/actions_std     | 0.803    |\n",
            "| rollout/episode_steps   | 1.04e+03 |\n",
            "| rollout/episodes        | 19       |\n",
            "| rollout/return          | 1.7      |\n",
            "| rollout/return_history  | 1.7      |\n",
            "| total/duration          | 132      |\n",
            "| total/episodes          | 19       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 152      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -1.94    |\n",
            "| train/loss_critic       | 0.0969   |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.0782   |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0        |\n",
            "| fps                | 238      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -1.45    |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 0.139    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 5.96e-08 |\n",
            "| fps                | 255      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -0.556   |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 1.34     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.00622 |\n",
            "| fps                | 257      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 43.2     |\n",
            "| policy_loss        | 0.109    |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 1.19     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0        |\n",
            "| fps                | 257      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 43.3     |\n",
            "| policy_loss        | -1.23    |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.289    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.639   |\n",
            "| fps                | 256      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 43.4     |\n",
            "| policy_loss        | -0.602   |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 0.0373   |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.378   |\n",
            "| fps                | 256      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 43.5     |\n",
            "| policy_loss        | -0.595   |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 0.462    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.00654  |\n",
            "| fps                | 258      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 43.6     |\n",
            "| policy_loss        | 0.875    |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 0.861    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0285  |\n",
            "| fps                | 260      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 43.7     |\n",
            "| policy_loss        | -1.06    |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 0.665    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0219  |\n",
            "| fps                | 261      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 43.7     |\n",
            "| policy_loss        | 0.988    |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 1.84     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0315   |\n",
            "| fps                | 261      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 43.8     |\n",
            "| policy_loss        | -0.132   |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 0.478    |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.3328232685724895  minutes\n",
            "======ACKTR Validation from:  20211018 to  20211223\n",
            "[270510.50025082065, 0.925955, 0.663375, 82.304, 1.059375, 0.72137, 88.8915, 124.0945, 1.5700450000000001, 1.453855, 1.04185, 0.8483799999999999, 129.2255, 10.059125, 1.6631349999999998, 10.284885000000001, 1.132685, 1.8506900000000002, 1.71367, 1.227425, 152.329, 1.960205, 1.3349600000000001, 77.708, 0.681045, 1.283555, 0.9199, 114.0985, 20.7163, 8.88048, 9.080925, 2679.0217514038086, 24.12010383605957, 0.0, 0.0, 2112.947007417679, 0.0, 0.0, 2518.980848312378, 0.0, 4699.176467895508, 0, 205.84898948669434, 2070.595766067505, 1581.3254146575928, 0.0, 1384.6041232347488, 144.6377935409546, 386.7792673110962, 0.0, 1375.1909538507462, 2124.6725780963898, 3541.172770023346, 1131.3796174526215, 0.0, 4235.824749469757, 0.0, 2557.8017575740814, 2490.274793624878, 190.8450222015381, 106.48291969299316, 0.001946629542349032, -0.002647948741719919, -0.25715064850497527, 0.004321646595687678, -0.0025211927116839394, -0.46642732598988346, 0.10491257825249534, 0.0015947309360375694, 0.004552617244518498, -0.0030949673984690396, 0.0006857486830068593, -0.26432148625423224, 0.02502599860692456, 0.00841670795067806, 0.045232640546204905, -0.002752194733127711, 0.0004371683521449121, 0.0040155461970030615, -0.00469888726230594, -0.4345347803499635, 0.008382229403729902, -0.004372002960830068, -0.5653042097046352, -0.005211899086457472, 0.007105641950504227, -0.00043667002905223296, 0.04430361019689144, -0.03348506542280916, 0.04370111305634339, 0.061395905261301564, 56.424307834683866, 47.27709595900803, 50.878519466553115, 58.147416969699776, 48.44287602467505, 47.72528467966952, 56.71309605588089, 48.056150134133986, 52.511430970698804, 39.79077118617295, 48.67832040680166, 49.29524184178347, 49.19627143265391, 53.10039947861868, 56.05512864913029, 45.19001504613836, 48.88472675882898, 54.22355740275549, 45.22647077397043, 50.3750979353672, 55.275391498707776, 46.415795270059924, 47.3440889993862, 44.013093470841184, 56.64931285284355, 48.53747138730294, 54.45340865020412, 48.90819935216643, 51.587914943554004, 57.060880192866, 0.9183515432331117, 0.659697333115836, 81.40681303317265, 1.0556333646469587, 0.7153490146141207, 88.64996993624817, 123.40045449089153, 1.5788030381017688, 1.4497503077084477, 1.041359907781153, 0.8509285900244317, 128.52393248780388, 10.147705307538715, 1.6665290555408616, 10.276192747998511, 1.129384813036538, 1.8553423920100223, 1.703859196072411, 1.2236880164853305, 151.04883250240636, 1.958582719406751, 1.327185195776158, 77.12182678850793, 0.677704473351549, 1.2837664341972161, 0.9222587659800043, 113.79829201385753, 20.898145163840955, 8.985997114187152, 9.099865163762678, 0.9120965, 0.6580705000000001, 81.0287000000001, 1.0506240000000013, 0.7131484999999992, 88.83859999999999, 123.131725, 1.5838057499999987, 1.4443487500000038, 1.0420802500000006, 0.8511327500000002, 128.3203000000001, 10.192025, 1.6639569999999995, 10.26839600000001, 1.1293909999999998, 1.860612499999999, 1.6970907500000005, 1.2243067500000007, 150.77634999999995, 1.9549235, 1.3269707499999996, 77.12492500000019, 0.6788175000000005, 1.2789092500000014, 0.9228044999999996, 113.60902500000024, 21.146722250000032, 9.023625500000009, 9.092276999999997]\n",
            "adjcp:  30\n",
            "======Trading from:  20211223 to  20220304\n",
            "previous_total_asset:1044444.3138934898\n",
            "end_total_asset:1051862.1798839946\n",
            "total_reward:7417.865990504855\n",
            "total_cost:  1171.669539347732\n",
            "total trades:  887\n",
            "Sharpe:  0.08837106110826394\n",
            "previous_total_asset:1044444.3138934898\n",
            "end_total_asset:1056747.1026976476\n",
            "total_reward:12302.78880415787\n",
            "total_cost:  915.807903272275\n",
            "total trades:  934\n",
            "Sharpe:  0.15693763590587181\n",
            "previous_total_asset:1044444.3138934898\n",
            "end_total_asset:1049603.7116639183\n",
            "total_reward:5159.397770428564\n",
            "total_cost:  2060.99192772154\n",
            "total trades:  1399\n",
            "Sharpe:  0.06246504315094135\n",
            "previous_total_asset:1044444.3138934898\n",
            "end_total_asset:1049801.26207181\n",
            "total_reward:5356.948178320192\n",
            "total_cost:  2453.483024894856\n",
            "total trades:  1273\n",
            "Sharpe:  0.07454230438873644\n",
            "previous_total_asset:1044444.3138934898\n",
            "end_total_asset:1047716.4292049926\n",
            "total_reward:3272.1153115028283\n",
            "total_cost:  2447.3755209904134\n",
            "total trades:  1256\n",
            "Sharpe:  0.044058637880204254\n",
            "============================================\n",
            "turbulence_threshold:  8928.58350008891\n",
            "[1000000, 0.927765, 0.664985, 82.8495, 1.060935, 0.724275, 89.3035, 124.5815, 1.56431, 1.451165, 1.0399, 0.844715, 129.57399999999998, 9.9915, 1.6594349999999998, 10.296155, 1.1327, 1.8519299999999999, 1.7181, 1.231475, 153.42700000000002, 1.96443, 1.341085, 78.0885, 0.682615, 1.28088, 0.91812, 114.39299999999999, 20.6338, 8.82103, 9.09005, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.002625043318522313, -0.0020341179842922053, -0.12143132929581668, 0.0044941569299137285, -0.001617112445904345, -0.38493029957182046, 0.19453091698409253, 0.0002982839492047251, 0.0044213422770555955, -0.003003073282892288, 0.00014072428117417601, -0.1601765372006696, 0.01047996168999532, 0.007312369167693733, 0.043893987494474374, -0.0022904014439038, 0.00010449358714126333, 0.004960963919476091, -0.00375772210668579, -0.21197000093565066, 0.008325515069491862, -0.002950966192549309, -0.4499276300458206, -0.004474122910459899, 0.006439451620550596, -0.0007464689589049556, 0.08990030518566527, -0.05408648002367045, 0.027162074647279155, 0.05649200569411228, 57.56027443355955, 48.23439886425339, 52.74818086269517, 59.02746285722563, 49.928200800319885, 49.380806691894584, 58.83956700839938, 46.59457802463444, 51.55967401859937, 38.48005074584415, 46.283046047326664, 50.70967772977401, 47.060818407675015, 52.015158564779966, 56.567672039178646, 45.19816954094288, 49.226788761196254, 55.57111752543847, 46.979648897252865, 53.30204055014406, 56.41068208424547, 49.00623893923155, 48.961973422694896, 44.99185022946553, 55.494884625017235, 47.52194679930246, 55.869344748149096, 47.73019084388399, 49.76754499021131, 57.37365788720493, 0.9200630808270915, 0.6606587270947749, 81.669119754414, 1.0565972983475116, 0.716971921047917, 88.76879358420304, 123.61519003800217, 1.5761679402650834, 1.45000752448873, 1.0410944700027618, 0.8497988463836259, 128.7148538536577, 10.119304342531676, 1.6652392272607048, 10.279822248362418, 1.129987574302622, 1.8547219570991091, 1.7064484331501544, 1.225103831669816, 151.48122659287793, 1.9596458613327963, 1.3297124329077656, 77.29758555423376, 0.6785972963785402, 1.2832416279795404, 0.921506263074549, 113.9064207386107, 20.850082406778963, 8.956003093425851, 9.098080588533099, 0.91301725, 0.6576710000000001, 81.0239000000001, 1.0512737500000013, 0.7134124999999992, 88.74302499999999, 123.19830000000002, 1.5840729999999987, 1.446023500000004, 1.0416377500000007, 0.8513037500000002, 128.3337250000001, 10.188514999999999, 1.6652244999999997, 10.27390875000001, 1.1299812499999997, 1.8605664999999987, 1.6987482500000006, 1.2235210000000005, 150.76327499999996, 1.9560385, 1.3274152499999996, 77.0715500000002, 0.6786467500000005, 1.2797150000000015, 0.9219094999999996, 113.56045000000024, 21.09961200000003, 9.01614200000001, 9.092421999999996]\n",
            "adjcp:  30\n",
            "======Model training from:  20180102 to  20211223\n",
            "======TD3 Training========\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 4         |\n",
            "| fps                     | 113       |\n",
            "| mean 100 episode reward | 0.6       |\n",
            "| n_updates               | 4200      |\n",
            "| qf1_loss                | 37552.234 |\n",
            "| qf2_loss                | 41029.383 |\n",
            "| time_elapsed            | 38        |\n",
            "| total timesteps         | 4380      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 8         |\n",
            "| fps                     | 107       |\n",
            "| mean 100 episode reward | 0.8       |\n",
            "| n_updates               | 8600      |\n",
            "| qf1_loss                | 96884.65  |\n",
            "| qf2_loss                | 74847.266 |\n",
            "| time_elapsed            | 81        |\n",
            "| total timesteps         | 8760      |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 12        |\n",
            "| fps                     | 103       |\n",
            "| mean 100 episode reward | 0.8       |\n",
            "| n_updates               | 13000     |\n",
            "| qf1_loss                | 47661.52  |\n",
            "| qf2_loss                | 27314.838 |\n",
            "| time_elapsed            | 126       |\n",
            "| total timesteps         | 13140     |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| current_lr              | 0.0003    |\n",
            "| episodes                | 16        |\n",
            "| fps                     | 103       |\n",
            "| mean 100 episode reward | 0.8       |\n",
            "| n_updates               | 17400     |\n",
            "| qf1_loss                | 47969.664 |\n",
            "| qf2_loss                | 26609.182 |\n",
            "| time_elapsed            | 169       |\n",
            "| total timesteps         | 17520     |\n",
            "---------------------------------------\n",
            "Training time (TD3):  3.226117193698883  minutes\n",
            "======TD3 Validation from:  20211223 to  20220304\n",
            "[0.21733812032639932, 0.92945, 0.67223, 84.631, 1.07779, 0.732915, 91.0475, 125.882, 1.51004, 1.4034200000000001, 1.0161, 0.82894, 127.77600000000001, 9.8689, 1.6275899999999999, 10.78945, 1.10665, 1.8211950000000001, 1.692635, 1.22443, 154.1175, 1.962925, 1.33487, 78.5165, 0.67997, 1.26811, 0.917175, 115.461, 20.64065, 8.91795, 9.74751, 0, 1900.0, 1900.0, 0, 0, 1900.0, 1967.0, 100.0, 0, 0, 0, 0.0, 2000.0, 2000.0, 2000.0, 0, 2000.0, 0, 0.0, 0, 0, 2000.0, 1900.0, 1924.0, 0, 1900.0, 1900.0, 0, 0, 0, 0.004475810266911129, 0.0026008861079699352, 0.45942741762566186, 0.0016981500844346353, 0.003321796725967441, 0.0561496249422504, 0.19631168409230781, -0.014778837130938749, -0.006557767453606367, -0.005611263800676003, -0.0016276511625271661, -0.515305647826068, -0.03930399215867375, -0.013195385739099175, 0.08241760690588151, -0.005476680438506465, -0.01407251808077703, -0.004542108355757302, -0.004483034521107321, -0.3157087040479496, -0.011982330850327738, -0.003936548739967183, 0.30325516519403095, 0.002010617500292189, 0.0003396798623940267, -0.0006526377029911767, 0.10231202250727733, 0.0019337721695436016, 0.008029798547090294, 0.11829325068621976, 60.27717721300287, 56.17773225229852, 58.35044134304661, 60.43126349394165, 56.78028851732437, 52.71794824265998, 56.24997677416894, 33.18529303093116, 38.54293456301783, 37.51195148978816, 42.58109271626373, 43.09062504882266, 42.93267815645586, 36.186651768130574, 64.34668309809987, 39.0594878652351, 35.98959478617128, 42.607641781891886, 41.94339414652379, 47.580775942158404, 39.85673383015762, 43.659857887619594, 54.69831192764595, 52.73540828298562, 49.73507544955663, 47.850487476042005, 54.149325893342635, 51.8631212888616, 52.17522761470542, 64.28498311098724, 0.9207166065844861, 0.666787703440376, 83.53032412757157, 1.0738349826406501, 0.724636043702035, 90.71890220149263, 125.27153719428212, 1.5460803469128512, 1.4234036402966412, 1.03118986540597, 0.833831754144495, 129.13084199388516, 9.954595688689965, 1.6603012130475516, 10.675134894741829, 1.1202814919160653, 1.8541006340878514, 1.707003636098777, 1.23626322980857, 154.855505583084, 1.9909248126108643, 1.3434658507008368, 77.78711265665218, 0.6748076717388045, 1.270565400770609, 0.9201347281246048, 115.2648337090002, 20.507559918167836, 8.885601754658618, 9.528139022753466, 0.9147470000000004, 0.6632559999999998, 82.93712500000008, 1.0736507500000014, 0.7192212499999991, 90.66377499999996, 125.057725, 1.572384749999999, 1.4382315000000037, 1.0429142500000004, 0.8371860000000005, 130.40047500000006, 10.037767500000001, 1.6882432499999993, 10.599822500000007, 1.1308314999999995, 1.8781480000000002, 1.717879250000001, 1.2455990000000003, 155.74902499999993, 2.0164972500000014, 1.350676999999999, 77.24932500000024, 0.6699092500000005, 1.2718140000000009, 0.9221489999999994, 115.31255000000024, 20.465200000000028, 8.876520000000003, 9.3740095]\n",
            "adjcp:  30\n",
            "TD3 Sharpe Ratio:  0.08189819385041855\n",
            "======PPO Training========\n",
            "Training time (PPO):  1.3634411454200746  minutes\n",
            "======PPO Validation from:  20211223 to  20220304\n",
            "[86645.41200692863, 0.92945, 0.67223, 84.631, 1.07779, 0.732915, 91.0475, 125.882, 1.51004, 1.4034200000000001, 1.0161, 0.82894, 127.77600000000001, 9.8689, 1.6275899999999999, 10.78945, 1.10665, 1.8211950000000001, 1.692635, 1.22443, 154.1175, 1.962925, 1.33487, 78.5165, 0.67997, 1.26811, 0.917175, 115.461, 20.64065, 8.91795, 9.74751, 2207.771382331848, 5.464230537414551, 3479.8011841773987, 560.6911177635193, 0.0, 2835.436887741089, 1009.5392866134644, 468.5275115966797, 96.76686239242554, 164.68812561035156, 787.336142539978, 0.0, 292.7620383501053, 592.8411507606506, 527.6422281265259, 1716.8642439842224, 85.72135162353516, 1052.6628049612045, 700.8783512115479, 831.5167121887207, 1712.2033956050873, 1039.6605405807495, 0.0, 426.792058467865, 104.81129741668701, 95.33641815185547, 524.5657916069031, 412.3719062805176, 233.0205215215683, 1675.6513786315918, 0.004475810266911129, 0.0026008861079699352, 0.45942741762566186, 0.0016981500844346353, 0.003321796725967441, 0.0561496249422504, 0.19631168409230781, -0.014778837130938749, -0.006557767453606367, -0.005611263800676003, -0.0016276511625271661, -0.515305647826068, -0.03930399215867375, -0.013195385739099175, 0.08241760690588151, -0.005476680438506465, -0.01407251808077703, -0.004542108355757302, -0.004483034521107321, -0.3157087040479496, -0.011982330850327738, -0.003936548739967183, 0.30325516519403095, 0.002010617500292189, 0.0003396798623940267, -0.0006526377029911767, 0.10231202250727733, 0.0019337721695436016, 0.008029798547090294, 0.11829325068621976, 60.27717721300287, 56.17773225229852, 58.35044134304661, 60.43126349394165, 56.78028851732437, 52.71794824265998, 56.24997677416894, 33.18529303093116, 38.54293456301783, 37.51195148978816, 42.58109271626373, 43.09062504882266, 42.93267815645586, 36.186651768130574, 64.34668309809987, 39.0594878652351, 35.98959478617128, 42.607641781891886, 41.94339414652379, 47.580775942158404, 39.85673383015762, 43.659857887619594, 54.69831192764595, 52.73540828298562, 49.73507544955663, 47.850487476042005, 54.149325893342635, 51.8631212888616, 52.17522761470542, 64.28498311098724, 0.9207166065844861, 0.666787703440376, 83.53032412757157, 1.0738349826406501, 0.724636043702035, 90.71890220149263, 125.27153719428212, 1.5460803469128512, 1.4234036402966412, 1.03118986540597, 0.833831754144495, 129.13084199388516, 9.954595688689965, 1.6603012130475516, 10.675134894741829, 1.1202814919160653, 1.8541006340878514, 1.707003636098777, 1.23626322980857, 154.855505583084, 1.9909248126108643, 1.3434658507008368, 77.78711265665218, 0.6748076717388045, 1.270565400770609, 0.9201347281246048, 115.2648337090002, 20.507559918167836, 8.885601754658618, 9.528139022753466, 0.9147470000000004, 0.6632559999999998, 82.93712500000008, 1.0736507500000014, 0.7192212499999991, 90.66377499999996, 125.057725, 1.572384749999999, 1.4382315000000037, 1.0429142500000004, 0.8371860000000005, 130.40047500000006, 10.037767500000001, 1.6882432499999993, 10.599822500000007, 1.1308314999999995, 1.8781480000000002, 1.717879250000001, 1.2455990000000003, 155.74902499999993, 2.0164972500000014, 1.350676999999999, 77.24932500000024, 0.6699092500000005, 1.2718140000000009, 0.9221489999999994, 115.31255000000024, 20.465200000000028, 8.876520000000003, 9.3740095]\n",
            "adjcp:  30\n",
            "PPO Sharpe Ratio:  0.11750304162534245\n",
            "======DDPG Training========\n",
            "Training time (DDPG):  2.3240498940149945  minutes\n",
            "======DDPG Validation from:  20211223 to  20220304\n",
            "[0.03447633218915769, 0.92945, 0.67223, 84.631, 1.07779, 0.732915, 91.0475, 125.882, 1.51004, 1.4034200000000001, 1.0161, 0.82894, 127.77600000000001, 9.8689, 1.6275899999999999, 10.78945, 1.10665, 1.8211950000000001, 1.692635, 1.22443, 154.1175, 1.962925, 1.33487, 78.5165, 0.67997, 1.26811, 0.917175, 115.461, 20.64065, 8.91795, 9.74751, 0, 0, 1700.0, 0, 0, 1599.9849243164062, 0, 1700.0, 0, 0, 0, 1693.9997634887695, 0, 0, 0, 1600.0, 1646.0, 0, 1700.0, 1700.0, 1699.999984741211, 1700.0, 0, 0, 0, 1700.0, 1700.0, 0, 1600.0, 0, 0.004475810266911129, 0.0026008861079699352, 0.45942741762566186, 0.0016981500844346353, 0.003321796725967441, 0.0561496249422504, 0.19631168409230781, -0.014778837130938749, -0.006557767453606367, -0.005611263800676003, -0.0016276511625271661, -0.515305647826068, -0.03930399215867375, -0.013195385739099175, 0.08241760690588151, -0.005476680438506465, -0.01407251808077703, -0.004542108355757302, -0.004483034521107321, -0.3157087040479496, -0.011982330850327738, -0.003936548739967183, 0.30325516519403095, 0.002010617500292189, 0.0003396798623940267, -0.0006526377029911767, 0.10231202250727733, 0.0019337721695436016, 0.008029798547090294, 0.11829325068621976, 60.27717721300287, 56.17773225229852, 58.35044134304661, 60.43126349394165, 56.78028851732437, 52.71794824265998, 56.24997677416894, 33.18529303093116, 38.54293456301783, 37.51195148978816, 42.58109271626373, 43.09062504882266, 42.93267815645586, 36.186651768130574, 64.34668309809987, 39.0594878652351, 35.98959478617128, 42.607641781891886, 41.94339414652379, 47.580775942158404, 39.85673383015762, 43.659857887619594, 54.69831192764595, 52.73540828298562, 49.73507544955663, 47.850487476042005, 54.149325893342635, 51.8631212888616, 52.17522761470542, 64.28498311098724, 0.9207166065844861, 0.666787703440376, 83.53032412757157, 1.0738349826406501, 0.724636043702035, 90.71890220149263, 125.27153719428212, 1.5460803469128512, 1.4234036402966412, 1.03118986540597, 0.833831754144495, 129.13084199388516, 9.954595688689965, 1.6603012130475516, 10.675134894741829, 1.1202814919160653, 1.8541006340878514, 1.707003636098777, 1.23626322980857, 154.855505583084, 1.9909248126108643, 1.3434658507008368, 77.78711265665218, 0.6748076717388045, 1.270565400770609, 0.9201347281246048, 115.2648337090002, 20.507559918167836, 8.885601754658618, 9.528139022753466, 0.9147470000000004, 0.6632559999999998, 82.93712500000008, 1.0736507500000014, 0.7192212499999991, 90.66377499999996, 125.057725, 1.572384749999999, 1.4382315000000037, 1.0429142500000004, 0.8371860000000005, 130.40047500000006, 10.037767500000001, 1.6882432499999993, 10.599822500000007, 1.1308314999999995, 1.8781480000000002, 1.717879250000001, 1.2455990000000003, 155.74902499999993, 2.0164972500000014, 1.350676999999999, 77.24932500000024, 0.6699092500000005, 1.2718140000000009, 0.9221489999999994, 115.31255000000024, 20.465200000000028, 8.876520000000003, 9.3740095]\n",
            "adjcp:  30\n",
            "======ACKTR Training========\n",
            "--------------------------------------\n",
            "| explained_variance      | 0        |\n",
            "| fps                     | 12       |\n",
            "| nupdates                | 1        |\n",
            "| policy_entropy          | 42.6     |\n",
            "| policy_loss             | 1.79     |\n",
            "| reference_Q_mean        | 0.903    |\n",
            "| reference_Q_std         | 0.642    |\n",
            "| reference_action_mean   | -0.115   |\n",
            "| reference_action_std    | 0.99     |\n",
            "| reference_actor_Q_mean  | 1.06     |\n",
            "| reference_actor_Q_std   | 0.687    |\n",
            "| rollout/Q_mean          | 0.407    |\n",
            "| rollout/actions_mean    | -0.108   |\n",
            "| rollout/actions_std     | 0.815    |\n",
            "| rollout/episode_steps   | 1.1e+03  |\n",
            "| rollout/episodes        | 18       |\n",
            "| rollout/return          | -1.39    |\n",
            "| rollout/return_history  | -1.39    |\n",
            "| total/duration          | 139      |\n",
            "| total/episodes          | 18       |\n",
            "| total/epochs            | 1        |\n",
            "| total/steps             | 19998    |\n",
            "| total/steps_per_second  | 144      |\n",
            "| total_timesteps         | 20       |\n",
            "| train/loss_actor        | -0.934   |\n",
            "| train/loss_critic       | 0.125    |\n",
            "| train/param_noise_di... | 0        |\n",
            "| value_loss              | 0.00266  |\n",
            "--------------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.18    |\n",
            "| fps                | 213      |\n",
            "| nupdates           | 100      |\n",
            "| policy_entropy     | 42.7     |\n",
            "| policy_loss        | -0.277   |\n",
            "| total_timesteps    | 2000     |\n",
            "| value_loss         | 0.149    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.0344  |\n",
            "| fps                | 228      |\n",
            "| nupdates           | 200      |\n",
            "| policy_entropy     | 42.8     |\n",
            "| policy_loss        | 0.765    |\n",
            "| total_timesteps    | 4000     |\n",
            "| value_loss         | 0.139    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.0898   |\n",
            "| fps                | 229      |\n",
            "| nupdates           | 300      |\n",
            "| policy_entropy     | 42.8     |\n",
            "| policy_loss        | 0.586    |\n",
            "| total_timesteps    | 6000     |\n",
            "| value_loss         | 0.13     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.124   |\n",
            "| fps                | 229      |\n",
            "| nupdates           | 400      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | 0.526    |\n",
            "| total_timesteps    | 8000     |\n",
            "| value_loss         | 0.17     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.11    |\n",
            "| fps                | 229      |\n",
            "| nupdates           | 500      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | -0.793   |\n",
            "| total_timesteps    | 10000    |\n",
            "| value_loss         | 0.641    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0        |\n",
            "| fps                | 230      |\n",
            "| nupdates           | 600      |\n",
            "| policy_entropy     | 42.9     |\n",
            "| policy_loss        | -1.83    |\n",
            "| total_timesteps    | 12000    |\n",
            "| value_loss         | 8.94     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.096   |\n",
            "| fps                | 230      |\n",
            "| nupdates           | 700      |\n",
            "| policy_entropy     | 43       |\n",
            "| policy_loss        | -1.5     |\n",
            "| total_timesteps    | 14000    |\n",
            "| value_loss         | 0.336    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.234    |\n",
            "| fps                | 229      |\n",
            "| nupdates           | 800      |\n",
            "| policy_entropy     | 43       |\n",
            "| policy_loss        | -1.23    |\n",
            "| total_timesteps    | 16000    |\n",
            "| value_loss         | 1.69     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | -0.202   |\n",
            "| fps                | 229      |\n",
            "| nupdates           | 900      |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -0.21    |\n",
            "| total_timesteps    | 18000    |\n",
            "| value_loss         | 4.08     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| explained_variance | 0.00994  |\n",
            "| fps                | 229      |\n",
            "| nupdates           | 1000     |\n",
            "| policy_entropy     | 43.1     |\n",
            "| policy_loss        | -0.231   |\n",
            "| total_timesteps    | 20000    |\n",
            "| value_loss         | 1.15     |\n",
            "---------------------------------\n",
            "Training time (ACKTR):  1.5150137424468995  minutes\n",
            "======ACKTR Validation from:  20211223 to  20220304\n",
            "[0.4328835144777514, 0.92945, 0.67223, 84.631, 1.07779, 0.732915, 91.0475, 125.882, 1.51004, 1.4034200000000001, 1.0161, 0.82894, 127.77600000000001, 9.8689, 1.6275899999999999, 10.78945, 1.10665, 1.8211950000000001, 1.692635, 1.22443, 154.1175, 1.962925, 1.33487, 78.5165, 0.67997, 1.26811, 0.917175, 115.461, 20.64065, 8.91795, 9.74751, 139.97131156921387, 3364.090783596039, 984.5967903137207, 699.2876515388489, 564.4800748825073, 2258.4113392829895, 149.23898601531982, 0.0, 0.0, 4160.527374267578, 209.8889193534851, 2332.463261604309, 3399.500535964966, 0.0, 3324.8165378570557, 10.547830581665039, 119.99262046813965, 3161.021446943283, 3654.142412185669, 1048.6255031228065, 0.0, 3263.4995188713074, 1156.878866314888, 3906.4305534362793, 4130.039318084717, 0.0, 0.0, 0.0, 1038.385936498642, 3021.322832584381, 0.004475810266911129, 0.0026008861079699352, 0.45942741762566186, 0.0016981500844346353, 0.003321796725967441, 0.0561496249422504, 0.19631168409230781, -0.014778837130938749, -0.006557767453606367, -0.005611263800676003, -0.0016276511625271661, -0.515305647826068, -0.03930399215867375, -0.013195385739099175, 0.08241760690588151, -0.005476680438506465, -0.01407251808077703, -0.004542108355757302, -0.004483034521107321, -0.3157087040479496, -0.011982330850327738, -0.003936548739967183, 0.30325516519403095, 0.002010617500292189, 0.0003396798623940267, -0.0006526377029911767, 0.10231202250727733, 0.0019337721695436016, 0.008029798547090294, 0.11829325068621976, 60.27717721300287, 56.17773225229852, 58.35044134304661, 60.43126349394165, 56.78028851732437, 52.71794824265998, 56.24997677416894, 33.18529303093116, 38.54293456301783, 37.51195148978816, 42.58109271626373, 43.09062504882266, 42.93267815645586, 36.186651768130574, 64.34668309809987, 39.0594878652351, 35.98959478617128, 42.607641781891886, 41.94339414652379, 47.580775942158404, 39.85673383015762, 43.659857887619594, 54.69831192764595, 52.73540828298562, 49.73507544955663, 47.850487476042005, 54.149325893342635, 51.8631212888616, 52.17522761470542, 64.28498311098724, 0.9207166065844861, 0.666787703440376, 83.53032412757157, 1.0738349826406501, 0.724636043702035, 90.71890220149263, 125.27153719428212, 1.5460803469128512, 1.4234036402966412, 1.03118986540597, 0.833831754144495, 129.13084199388516, 9.954595688689965, 1.6603012130475516, 10.675134894741829, 1.1202814919160653, 1.8541006340878514, 1.707003636098777, 1.23626322980857, 154.855505583084, 1.9909248126108643, 1.3434658507008368, 77.78711265665218, 0.6748076717388045, 1.270565400770609, 0.9201347281246048, 115.2648337090002, 20.507559918167836, 8.885601754658618, 9.528139022753466, 0.9147470000000004, 0.6632559999999998, 82.93712500000008, 1.0736507500000014, 0.7192212499999991, 90.66377499999996, 125.057725, 1.572384749999999, 1.4382315000000037, 1.0429142500000004, 0.8371860000000005, 130.40047500000006, 10.037767500000001, 1.6882432499999993, 10.599822500000007, 1.1308314999999995, 1.8781480000000002, 1.717879250000001, 1.2455990000000003, 155.74902499999993, 2.0164972500000014, 1.350676999999999, 77.24932500000024, 0.6699092500000005, 1.2718140000000009, 0.9221489999999994, 115.31255000000024, 20.465200000000028, 8.876520000000003, 9.3740095]\n",
            "adjcp:  30\n",
            "======Trading from:  20220304 to  20220513\n",
            "previous_total_asset:1047716.4292049926\n",
            "end_total_asset:1106269.9342151221\n",
            "total_reward:58553.505010129535\n",
            "total_cost:  806.0663928291326\n",
            "total trades:  1046\n",
            "Sharpe:  0.3637547264301879\n",
            "previous_total_asset:1047716.4292049926\n",
            "end_total_asset:1103623.8662315141\n",
            "total_reward:55907.43702652154\n",
            "total_cost:  508.6308508454732\n",
            "total trades:  905\n",
            "Sharpe:  0.33710485879353985\n",
            "previous_total_asset:1047716.4292049926\n",
            "end_total_asset:1095799.8480342624\n",
            "total_reward:48083.41882926982\n",
            "total_cost:  2649.141506728312\n",
            "total trades:  1418\n",
            "Sharpe:  0.3189700207759108\n",
            "previous_total_asset:1047716.4292049926\n",
            "end_total_asset:1095520.3480100378\n",
            "total_reward:47803.91880504519\n",
            "total_cost:  2180.3826160134845\n",
            "total trades:  1344\n",
            "Sharpe:  0.31442307604851855\n",
            "previous_total_asset:1047716.4292049926\n",
            "end_total_asset:1095136.1439708378\n",
            "total_reward:47419.714765845216\n",
            "total_cost:  2499.056933151613\n",
            "total trades:  1416\n",
            "Sharpe:  0.318284603691509\n",
            "Ensemble Strategy took:  71.30315857330957  minutes\n",
            "[0d6b48fae0d1:01356] *** Process received signal ***\n",
            "[0d6b48fae0d1:01356] Signal: Segmentation fault (11)\n",
            "[0d6b48fae0d1:01356] Signal code: Address not mapped (1)\n",
            "[0d6b48fae0d1:01356] Failing at address: 0x7f9611d0820d\n",
            "[0d6b48fae0d1:01356] [ 0] /lib/x86_64-linux-gnu/libpthread.so.0(+0x12980)[0x7f9614bb4980]\n",
            "[0d6b48fae0d1:01356] [ 1] /lib/x86_64-linux-gnu/libc.so.6(getenv+0xa5)[0x7f96147f3775]\n",
            "[0d6b48fae0d1:01356] [ 2] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(_ZN13TCMallocGuardD1Ev+0x34)[0x7f961505ee44]\n",
            "[0d6b48fae0d1:01356] [ 3] /lib/x86_64-linux-gnu/libc.so.6(__cxa_finalize+0xf5)[0x7f96147f4605]\n",
            "[0d6b48fae0d1:01356] [ 4] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(+0x13cb3)[0x7f961505ccb3]\n",
            "[0d6b48fae0d1:01356] *** End of error message ***\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "print(os.listdir())\n",
        "list_value_PPO =[]\n",
        "list_value_ACKTR =[]\n",
        "list_value_DDPG =[]\n",
        "list_value_TD3 =[]\n",
        "list_value_Ensemble =[]\n",
        "plt.rcParams[\"figure.figsize\"] = (20,12)\n",
        "\n",
        "%cd /content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/PPO\n",
        "list_file = []\n",
        "for file in os.listdir():\n",
        "  cl = []\n",
        "  if file[-3:] == 'csv' and file[8:13] == 'value':\n",
        "    list_file.append(file)\n",
        "list_file = sorted(list_file)\n",
        "print(list_file)\n",
        "for file in list_file:\n",
        "    f = pd.read_csv(f'{file}')\n",
        "    list_att = f['0'].tolist()\n",
        "    for attribute in list_att:\n",
        "      list_value_PPO.append(attribute)\n",
        "count = len(list_value_PPO)\n",
        "for i in range(0, count, 1):\n",
        "  cl.append(i)\n",
        "print(cl)\n",
        "%cd /content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/TD3\n",
        "list_file = []\n",
        "for file in os.listdir():\n",
        "  cl = []\n",
        "  if file[-3:] == 'csv' and file[8:13] == 'value':\n",
        "    list_file.append(file)\n",
        "list_file = sorted(list_file)\n",
        "print(list_file)\n",
        "for file in list_file:\n",
        "    f = pd.read_csv(f'{file}')\n",
        "    list_att = f['0'].tolist()\n",
        "    for attribute in list_att:\n",
        "      list_value_TD3.append(attribute)\n",
        "count = len(list_value_TD3)\n",
        "for i in range(0, count, 1):\n",
        "  cl.append(i)\n",
        "%cd /content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/DDPG\n",
        "list_file = []\n",
        "for file in os.listdir():\n",
        "  cl = []\n",
        "  if file[-3:] == 'csv' and file[8:13] == 'value':\n",
        "    list_file.append(file)\n",
        "list_file = sorted(list_file)\n",
        "print(list_file)\n",
        "for file in list_file:\n",
        "    f = pd.read_csv(f'{file}')\n",
        "    list_att = f['0'].tolist()\n",
        "    for attribute in list_att:\n",
        "      list_value_DDPG.append(attribute)\n",
        "count = len(list_value_DDPG)\n",
        "for i in range(0, count, 1):\n",
        "  cl.append(i)\n",
        "%cd /content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/ACKTR\n",
        "list_file = []\n",
        "for file in os.listdir():\n",
        "  cl = []\n",
        "  if file[-3:] == 'csv' and file[8:13] == 'value':\n",
        "    list_file.append(file)\n",
        "list_file = sorted(list_file)\n",
        "print(list_file)\n",
        "for file in list_file:\n",
        "    f = pd.read_csv(f'{file}')\n",
        "    list_att = f['0'].tolist()\n",
        "    for attribute in list_att:\n",
        "      list_value_ACKTR.append(attribute)\n",
        "count = len(list_value_ACKTR)\n",
        "for i in range(0, count, 1):\n",
        "  cl.append(i)\n",
        "%cd /content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/ensemble\n",
        "list_file = []\n",
        "for file in os.listdir():\n",
        "  cl = []\n",
        "  if file[-3:] == 'csv' and file[8:13] == 'value':\n",
        "    list_file.append(file)\n",
        "list_file = sorted(list_file)\n",
        "print(list_file)\n",
        "for file in list_file:\n",
        "    f = pd.read_csv(f'{file}')\n",
        "    list_att = f['0'].tolist()\n",
        "    for attribute in list_att:\n",
        "      list_value_Ensemble.append(attribute)\n",
        "count = len(list_value_Ensemble)\n",
        "for i in range(0, count, 1):\n",
        "  cl.append(i)\n",
        "plt.plot(cl, list_value_PPO, label = \"PPO\")\n",
        "plt.plot(cl, list_value_TD3, label = \"TD3\")\n",
        "plt.plot(cl, list_value_DDPG, label = \"DDPG\")\n",
        "plt.plot(cl, list_value_ACKTR, label = \"ACKTR\")\n",
        "plt.plot(cl, list_value_Ensemble, label = \"Ensemble method\", marker ='o', markersize = 3)\n",
        "plt.title('Số dư tài khoản của các \\'Trader\\' Deep Reiforcement Learning sau trong thời điểm 07/2020 tới 07/2022')\n",
        "plt.xlabel('Khoảng thời gian (Ngày)')\n",
        "plt.ylabel('Số dư tài khoản (1000000 USD)')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "df = pd.DataFrame({'PPO':list_value_PPO,\n",
        "                   'TD3':list_value_TD3,\n",
        "                   'DDPG':list_value_DDPG,\n",
        "                   'ACKTR':list_value_ACKTR,\n",
        "                   'Ensemble': list_value_Ensemble})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D0STnQkko6LU",
        "outputId": "96354e7d-2771-4cbf-93db-a8d6755389aa"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['README.md', 'preprocessing', 'model', '.git', '__init__.py', 'env', 'config', 'data', 'trained_models', 'run_DRL.py', 'results', 'requirements.txt', 'compare_model.csv']\n",
            "/content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/PPO\n",
            "['account_value_trade_PPO_100.csv', 'account_value_trade_PPO_150.csv', 'account_value_trade_PPO_200.csv', 'account_value_trade_PPO_250.csv', 'account_value_trade_PPO_300.csv', 'account_value_trade_PPO_350.csv', 'account_value_trade_PPO_400.csv', 'account_value_trade_PPO_450.csv', 'account_value_trade_PPO_500.csv']\n",
            "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449]\n",
            "/content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/TD3\n",
            "['account_value_trade_TD3_100.csv', 'account_value_trade_TD3_150.csv', 'account_value_trade_TD3_200.csv', 'account_value_trade_TD3_250.csv', 'account_value_trade_TD3_300.csv', 'account_value_trade_TD3_350.csv', 'account_value_trade_TD3_400.csv', 'account_value_trade_TD3_450.csv', 'account_value_trade_TD3_500.csv']\n",
            "/content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/DDPG\n",
            "['account_value_trade_DDPG_100.csv', 'account_value_trade_DDPG_150.csv', 'account_value_trade_DDPG_200.csv', 'account_value_trade_DDPG_250.csv', 'account_value_trade_DDPG_300.csv', 'account_value_trade_DDPG_350.csv', 'account_value_trade_DDPG_400.csv', 'account_value_trade_DDPG_450.csv', 'account_value_trade_DDPG_500.csv']\n",
            "/content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/ACKTR\n",
            "['account_value_trade_ACKTR_100.csv', 'account_value_trade_ACKTR_150.csv', 'account_value_trade_ACKTR_200.csv', 'account_value_trade_ACKTR_250.csv', 'account_value_trade_ACKTR_300.csv', 'account_value_trade_ACKTR_350.csv', 'account_value_trade_ACKTR_400.csv', 'account_value_trade_ACKTR_450.csv', 'account_value_trade_ACKTR_500.csv']\n",
            "/content/Forex-Trading-Automation-with-Deep-Reinforcement-Learning/results/ensemble\n",
            "['account_value_trade_ensemble_100.csv', 'account_value_trade_ensemble_150.csv', 'account_value_trade_ensemble_200.csv', 'account_value_trade_ensemble_250.csv', 'account_value_trade_ensemble_300.csv', 'account_value_trade_ensemble_350.csv', 'account_value_trade_ensemble_400.csv', 'account_value_trade_ensemble_450.csv', 'account_value_trade_ensemble_500.csv']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              PPO           TD3          DDPG         ACKTR      Ensemble\n",
              "0    1.000000e+06  1.000000e+06  1.000000e+06  1.000000e+06  1.000000e+06\n",
              "1    9.999966e+05  9.999854e+05  1.000030e+06  1.000007e+06  9.999854e+05\n",
              "2    9.998707e+05  9.996613e+05  9.998537e+05  9.999607e+05  9.996613e+05\n",
              "3    9.996715e+05  9.993095e+05  9.995796e+05  9.999503e+05  9.993095e+05\n",
              "4    9.991930e+05  9.983821e+05  9.989566e+05  9.998751e+05  9.983821e+05\n",
              "..            ...           ...           ...           ...           ...\n",
              "445  1.123848e+06  1.136814e+06  1.133032e+06  1.125041e+06  1.124881e+06\n",
              "446  1.119561e+06  1.131643e+06  1.129929e+06  1.121186e+06  1.118622e+06\n",
              "447  1.118936e+06  1.131308e+06  1.129680e+06  1.120593e+06  1.118176e+06\n",
              "448  1.114940e+06  1.127338e+06  1.123345e+06  1.115517e+06  1.114022e+06\n",
              "449  1.095800e+06  1.106270e+06  1.103624e+06  1.095520e+06  1.095136e+06\n",
              "\n",
              "[450 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15e3e921-1731-4189-a052-8a7480640656\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PPO</th>\n",
              "      <th>TD3</th>\n",
              "      <th>DDPG</th>\n",
              "      <th>ACKTR</th>\n",
              "      <th>Ensemble</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.999966e+05</td>\n",
              "      <td>9.999854e+05</td>\n",
              "      <td>1.000030e+06</td>\n",
              "      <td>1.000007e+06</td>\n",
              "      <td>9.999854e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.998707e+05</td>\n",
              "      <td>9.996613e+05</td>\n",
              "      <td>9.998537e+05</td>\n",
              "      <td>9.999607e+05</td>\n",
              "      <td>9.996613e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.996715e+05</td>\n",
              "      <td>9.993095e+05</td>\n",
              "      <td>9.995796e+05</td>\n",
              "      <td>9.999503e+05</td>\n",
              "      <td>9.993095e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.991930e+05</td>\n",
              "      <td>9.983821e+05</td>\n",
              "      <td>9.989566e+05</td>\n",
              "      <td>9.998751e+05</td>\n",
              "      <td>9.983821e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445</th>\n",
              "      <td>1.123848e+06</td>\n",
              "      <td>1.136814e+06</td>\n",
              "      <td>1.133032e+06</td>\n",
              "      <td>1.125041e+06</td>\n",
              "      <td>1.124881e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>446</th>\n",
              "      <td>1.119561e+06</td>\n",
              "      <td>1.131643e+06</td>\n",
              "      <td>1.129929e+06</td>\n",
              "      <td>1.121186e+06</td>\n",
              "      <td>1.118622e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>447</th>\n",
              "      <td>1.118936e+06</td>\n",
              "      <td>1.131308e+06</td>\n",
              "      <td>1.129680e+06</td>\n",
              "      <td>1.120593e+06</td>\n",
              "      <td>1.118176e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>448</th>\n",
              "      <td>1.114940e+06</td>\n",
              "      <td>1.127338e+06</td>\n",
              "      <td>1.123345e+06</td>\n",
              "      <td>1.115517e+06</td>\n",
              "      <td>1.114022e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449</th>\n",
              "      <td>1.095800e+06</td>\n",
              "      <td>1.106270e+06</td>\n",
              "      <td>1.103624e+06</td>\n",
              "      <td>1.095520e+06</td>\n",
              "      <td>1.095136e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>450 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15e3e921-1731-4189-a052-8a7480640656')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-15e3e921-1731-4189-a052-8a7480640656 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-15e3e921-1731-4189-a052-8a7480640656');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "gtwg0DfOMQ7S",
        "outputId": "38b1e7db-a358-42e2-993b-124448f18c7e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                PPO           TD3          DDPG         ACKTR      Ensemble\n",
              "count  4.500000e+02  4.500000e+02  4.500000e+02  4.500000e+02  4.500000e+02\n",
              "mean   1.038358e+06  1.038603e+06  1.038199e+06  1.038565e+06  1.037816e+06\n",
              "std    2.987996e+04  3.123174e+04  3.227860e+04  2.930885e+04  2.947331e+04\n",
              "min    9.907860e+05  9.911216e+05  9.850189e+05  9.917590e+05  9.911216e+05\n",
              "25%    1.019087e+06  1.017061e+06  1.016176e+06  1.019274e+06  1.016176e+06\n",
              "50%    1.039108e+06  1.037796e+06  1.037238e+06  1.039059e+06  1.038790e+06\n",
              "75%    1.049316e+06  1.047679e+06  1.049807e+06  1.049795e+06  1.047772e+06\n",
              "max    1.134868e+06  1.143402e+06  1.147586e+06  1.133924e+06  1.132568e+06"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30e978d3-5d29-4f07-8d41-77c45cd4665c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PPO</th>\n",
              "      <th>TD3</th>\n",
              "      <th>DDPG</th>\n",
              "      <th>ACKTR</th>\n",
              "      <th>Ensemble</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.500000e+02</td>\n",
              "      <td>4.500000e+02</td>\n",
              "      <td>4.500000e+02</td>\n",
              "      <td>4.500000e+02</td>\n",
              "      <td>4.500000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.038358e+06</td>\n",
              "      <td>1.038603e+06</td>\n",
              "      <td>1.038199e+06</td>\n",
              "      <td>1.038565e+06</td>\n",
              "      <td>1.037816e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.987996e+04</td>\n",
              "      <td>3.123174e+04</td>\n",
              "      <td>3.227860e+04</td>\n",
              "      <td>2.930885e+04</td>\n",
              "      <td>2.947331e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>9.907860e+05</td>\n",
              "      <td>9.911216e+05</td>\n",
              "      <td>9.850189e+05</td>\n",
              "      <td>9.917590e+05</td>\n",
              "      <td>9.911216e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.019087e+06</td>\n",
              "      <td>1.017061e+06</td>\n",
              "      <td>1.016176e+06</td>\n",
              "      <td>1.019274e+06</td>\n",
              "      <td>1.016176e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.039108e+06</td>\n",
              "      <td>1.037796e+06</td>\n",
              "      <td>1.037238e+06</td>\n",
              "      <td>1.039059e+06</td>\n",
              "      <td>1.038790e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.049316e+06</td>\n",
              "      <td>1.047679e+06</td>\n",
              "      <td>1.049807e+06</td>\n",
              "      <td>1.049795e+06</td>\n",
              "      <td>1.047772e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.134868e+06</td>\n",
              "      <td>1.143402e+06</td>\n",
              "      <td>1.147586e+06</td>\n",
              "      <td>1.133924e+06</td>\n",
              "      <td>1.132568e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30e978d3-5d29-4f07-8d41-77c45cd4665c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-30e978d3-5d29-4f07-8d41-77c45cd4665c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-30e978d3-5d29-4f07-8d41-77c45cd4665c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df_melt = pd.melt(df.reset_index(), id_vars=['index'], value_vars=['PPO', 'TD3', 'DDPG', 'ACKTR','Ensemble'])\n",
        "# replace column names\n",
        "df_melt.columns = ['index', 'treatments', 'value']\n",
        "\n",
        "# generate a boxplot to see the data distribution by treatments. Using boxplot, we can \n",
        "# easily detect the differences between different treatments\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "ax = sns.boxplot(x='treatments', y='value', data=df_melt, color='#99c2a2')\n",
        "# ax = sns.swarmplot(x=\"treatments\", y=\"value\", data=df_melt, color='#7d0013')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "O-GgG3c82H8r",
        "outputId": "877b8af1-ef20-44c5-c549-418b37103a48"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vungo = pd.read_csv('/content/30-D1-20182022.csv')\n",
        "vungo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "DX_rG7vV5nUc",
        "outputId": "bb99fa7d-d050-4d32-95ea-909249f331b7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  datadate     tic       adjcp        open        high  \\\n",
              "0               0  20180102  AUDCAD    0.979655    0.982285    0.982895   \n",
              "1               1  20180102  AUDCHF    0.760795    0.760445    0.763820   \n",
              "2               2  20180102  AUDJPY   87.921500   87.908000   88.404500   \n",
              "3               3  20180102  AUDNZD    1.102185    1.098575    1.102670   \n",
              "4               4  20180102  AUDUSD    0.782990    0.780530    0.784475   \n",
              "...           ...       ...     ...         ...         ...         ...   \n",
              "36925       36925  20220701  USDCHF    0.959325    0.955065    0.964190   \n",
              "36926       36926  20220701  USDJPY  135.250000  135.727000  135.988500   \n",
              "36927       36927  20220701  USDMXN   20.259850   20.111550   20.466250   \n",
              "36928       36928  20220701  USDNOK    9.935300    9.873700   10.001310   \n",
              "36929       36929  20220701  USDSEK   10.308250   10.244100   10.376900   \n",
              "\n",
              "              low  volume      macd         rsi         ema        boll  \\\n",
              "0        0.978135   92743  0.000000  100.000000    0.979655    0.979655   \n",
              "1        0.759165   79787  0.000000  100.000000    0.760795    0.760795   \n",
              "2       87.632500  115328  0.000000  100.000000   87.921500   87.921500   \n",
              "3        1.097805  104294  0.000000  100.000000    1.102185    1.102185   \n",
              "4        0.779460   85073  0.000000  100.000000    0.782990    0.782990   \n",
              "...           ...     ...       ...         ...         ...         ...   \n",
              "36925    0.954450  261758 -0.004399   48.240017    0.960984    0.970884   \n",
              "36926  134.743000  498495  1.522156   61.694219  135.405253  134.782925   \n",
              "36927   20.085000  129114  0.048922   52.894210   20.113004   20.066005   \n",
              "36928    9.839295  316977  0.091385   58.733179    9.870282    9.813441   \n",
              "36929   10.209100  286632  0.091149   60.852986   10.178243   10.093694   \n",
              "\n",
              "       turbulence  \n",
              "0        0.000000  \n",
              "1        0.000000  \n",
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          "metadata": {},
          "execution_count": 7
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}