FinRL_Weights_and_Biasify_FinRL_for_Stable_Baselines3_models.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL/blob/master/FinRL_Weights_and_Biasify_FinRL_for_Stable_Baselines3_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7tNSkCdnxu-"
      },
      "source": [
        "%%capture\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
        "!pip install wandb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8l_5ypFddser"
      },
      "source": [
        "# %%capture\n",
        "# !pip install torch==1.4.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UL_DUpELpVyK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d86e828b-3f58-4719-dd24-7cece4331754"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.apps import config\n",
        "from finrl.finrl_meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.finrl_meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.finrl_meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.finrl_meta.env_stock_trading.env_stocktrading_np import StockTradingEnv as StockTradingEnv_numpy \n",
        "# from finrl.drl_agents.stablebaselines3.models import DRLAgent as DRLAgent_sb3\n",
        "from finrl.finrl_meta.data_processor import DataProcessor\n",
        "\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "import ray\n",
        "from pprint import pprint\n",
        "import pprint\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-WdUod78OtH"
      },
      "source": [
        "import wandb\n",
        "from wandb.integration.sb3 import WandbCallback"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhLdgzQ7ry-Z",
        "outputId": "d4748d6a-c05a-47e0-8e19-a4f980034428"
      },
      "source": [
        "wandb.login()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mathe_kunal\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcuU0wz6ppVF"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csBR7bjGm9CY"
      },
      "source": [
        "def model_params(model_name):\n",
        "  sweep_config = {\n",
        "      'method': 'bayes'\n",
        "          }\n",
        "\n",
        "  metric = {\n",
        "      'name': 'Val sharpe',\n",
        "      'goal': 'maximize'\n",
        "  }\n",
        "\n",
        "  sweep_config['metric'] = metric\n",
        "\n",
        "  ddpg_param_dict = {\n",
        "    \"buffer_size\": {\n",
        "        \"values\":[int(1e4), int(1e5), int(1e6)]\n",
        "        },     \n",
        "    \"learning_rate\": {   \n",
        "        \"distribution\": \"log_uniform\",\n",
        "        \"min\": 1e-5,\n",
        "        \"max\": 1,\n",
        "    },\n",
        "    \"batch_size\" :{\n",
        "        'values':[32, 64, 128, 256, 512]\n",
        "    },\n",
        "  }\n",
        "\n",
        "  a2c_param_dict = {\n",
        "      \"n_steps\": {\n",
        "          'values': [128, 256, 512, 1024, 2048]},\n",
        "      \"ent_coef\": {   \n",
        "        \"distribution\": \"log_uniform\",\n",
        "        \"min\": 1e-8,\n",
        "        \"max\": 1,\n",
        "    },\n",
        "      \"learning_rate\": {   \n",
        "        \"distribution\": \"log_uniform\",\n",
        "        \"min\": 1e-5,\n",
        "        \"max\": 1,\n",
        "    },\n",
        "  }\n",
        "\n",
        "  ppo_param_dict = {\n",
        "      \"ent_coef\": {   \n",
        "        \"distribution\": \"log_uniform\",\n",
        "        \"min\": 1e-8,\n",
        "        \"max\": 1,\n",
        "    },\n",
        "        \"n_steps\": {\n",
        "            'values':[128, 256, 512, 1024, 2048]},\n",
        "        \"learning_rate\": {   \n",
        "        \"distribution\": \"log_uniform\",\n",
        "        \"min\": 1e-2,\n",
        "        \"max\": 1,\n",
        "    },\n",
        "        \"batch_size\": {\n",
        "        'values':[32, 64, 128, 256, 512]\n",
        "    },\n",
        "  }\n",
        "\n",
        "  stopping_criteria = {'type': 'hyperband', 's': 2, 'eta': 2, 'max_iter': 12}\n",
        "\n",
        "  sweep_config['early_terminate'] = stopping_criteria\n",
        "\n",
        "  if model_name == 'ddpg':\n",
        "    sweep_config['parameters'] = ddpg_param_dict\n",
        "  elif model_name == 'ppo':\n",
        "    sweep_config['parameters'] = ppo_param_dict\n",
        "  elif model_name == 'a2c':\n",
        "    sweep_config['parameters'] = a2c_param_dict\n",
        "\n",
        "  return sweep_config"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gdiVZzPG7v2y",
        "outputId": "eeb9f3b1-c930-4b35-fa08-9736b3bbb6fa"
      },
      "source": [
        "%%writefile model_wandb.py\n",
        "import wandb\n",
        "from wandb.integration.sb3 import WandbCallback\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from finrl.apps import config\n",
        "# from finrl.finrl_meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.finrl_meta.preprocessor.preprocessors import data_split\n",
        "from stable_baselines3 import A2C, DDPG, PPO, SAC, TD3\n",
        "from stable_baselines3.common.callbacks import BaseCallback\n",
        "from stable_baselines3.common.noise import (\n",
        "    NormalActionNoise,\n",
        "    OrnsteinUhlenbeckActionNoise,\n",
        ")\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "import pprint\n",
        "MODELS = {\"a2c\": A2C, \"ddpg\": DDPG, \"td3\": TD3, \"sac\": SAC, \"ppo\": PPO}\n",
        "\n",
        "MODEL_KWARGS = {x: config.__dict__[f\"{x.upper()}_PARAMS\"] for x in MODELS.keys()}\n",
        "\n",
        "NOISE = {\n",
        "    \"normal\": NormalActionNoise,\n",
        "    \"ornstein_uhlenbeck\": OrnsteinUhlenbeckActionNoise,\n",
        "}\n",
        " \n",
        "class DRLAgent_SB3:\n",
        "  def __init__(self,env,run):\n",
        "    self.env = env\n",
        "    # self.run = wandb.init(reinit=True,\n",
        "    #       project = 'finrl-sweeps-sb3',\n",
        "    #       sync_tensorboard = True,\n",
        "    #       save_code = True\n",
        "    #   )\n",
        "    self.run = run\n",
        "  def get_model(\n",
        "      self,\n",
        "      model_name,\n",
        "      policy_kwargs=None,\n",
        "      model_kwargs=None,\n",
        "      verbose=1,\n",
        "      seed=None,\n",
        "  ):\n",
        "      if model_name not in MODELS:\n",
        "          raise NotImplementedError(\"NotImplementedError\")\n",
        "\n",
        "      if model_kwargs is None:\n",
        "          model_kwargs = MODEL_KWARGS[model_name]\n",
        "\n",
        "      if \"action_noise\" in model_kwargs:\n",
        "          n_actions = self.env.action_space.shape[-1]\n",
        "          model_kwargs[\"action_noise\"] = NOISE[model_kwargs[\"action_noise\"]](\n",
        "              mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions)\n",
        "          )\n",
        "      print(model_kwargs)\n",
        "\n",
        "      model = MODELS[model_name](\n",
        "          policy='MlpPolicy',\n",
        "          env=self.env,\n",
        "          tensorboard_log=f\"runs/{self.run.id}\",\n",
        "          verbose=verbose,\n",
        "          policy_kwargs=policy_kwargs,\n",
        "          seed=seed,\n",
        "          **model_kwargs,\n",
        "      )\n",
        "      return model\n",
        "  \n",
        "  def train_model(self, model,total_timesteps):\n",
        "    model = model.learn(\n",
        "        total_timesteps=total_timesteps,\n",
        "        callback = WandbCallback(\n",
        "            gradient_save_freq = 100, model_save_path = f\"models/{self.run.id}\",\n",
        "            verbose = 2\n",
        "        ),\n",
        "    )\n",
        "    \n",
        "    return model\n",
        "  @staticmethod\n",
        "  def DRL_prediction_load_from_file(run , model_name, environment,val_or_test='val'):\n",
        "      if model_name not in MODELS:\n",
        "          raise NotImplementedError(\"NotImplementedError, Pass correct model name\")\n",
        "      try:\n",
        "          # load agent\n",
        "          model = MODELS[model_name].load(f\"models/{run.id}/model.zip\") #print_system_info=True\n",
        "          print(\"Successfully load model\", f\"models/{run.id}\")\n",
        "      except BaseException:\n",
        "          raise ValueError(\"Fail to load agent!\")\n",
        "\n",
        "      # test on the testing env\n",
        "      state = environment.reset()\n",
        "      episode_returns = list()  # the cumulative_return / initial_account\n",
        "      episode_total_assets = list()\n",
        "      episode_total_assets.append(environment.initial_total_asset)\n",
        "      done = False\n",
        "      while not done:\n",
        "          action = model.predict(state)[0]\n",
        "          state, reward, done, _ = environment.step(action)\n",
        "\n",
        "          total_asset = (\n",
        "              environment.amount\n",
        "              + (environment.price_ary[environment.day] * environment.stocks).sum()\n",
        "          )\n",
        "          episode_total_assets.append(total_asset)\n",
        "          episode_return = total_asset / environment.initial_total_asset\n",
        "          episode_returns.append(episode_return)\n",
        "    \n",
        "      def calculate_sharpe(df):\n",
        "        df['daily_return'] = df['account_value'].pct_change(1)\n",
        "        if df['daily_return'].std() !=0:\n",
        "          sharpe = (252**0.5)*df['daily_return'].mean()/ \\\n",
        "              df['daily_return'].std()\n",
        "          return sharpe\n",
        "        else:\n",
        "          return 0\n",
        "\n",
        "      print(\"episode_return\", episode_return)\n",
        "      print(\"Test Finished!\")\n",
        "      sharpe_df = pd.DataFrame(episode_total_assets,columns=['account_value'])\n",
        "      sharpe = calculate_sharpe(sharpe_df)\n",
        "      if val_or_test == \"val\":\n",
        "        wandb.log({\"Val sharpe\":sharpe})\n",
        "      elif val_or_test == \"test\":\n",
        "        wandb.log({\"Test sharpe\":sharpe})\n",
        "        print(f'Test Sharpe for {run.id} is {sharpe}')\n",
        "      # run.finish()\n",
        "      return sharpe, episode_total_assets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting model_wandb.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZwuaatxW1oJ"
      },
      "source": [
        "from model_wandb import DRLAgent_SB3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5m0ZdifPpqVX"
      },
      "source": [
        "def train_agent_env(start_date, end_date, ticker_list, data_source, time_interval, \n",
        "          technical_indicator_list, env, model_name, if_vix = True,\n",
        "          **kwargs):\n",
        "    \n",
        "    #fetch data\n",
        "    DP = DataProcessor(data_source, **kwargs)\n",
        "    data = DP.download_data(ticker_list, start_date, end_date, time_interval)\n",
        "    data = DP.clean_data(data)\n",
        "    data = DP.add_technical_indicator(data, technical_indicator_list)\n",
        "    if if_vix:\n",
        "        data = DP.add_vix(data)\n",
        "    # data.to_csv('train_data.csv')\n",
        "    # data = pd.read_csv('train_data.csv')\n",
        "    price_array, tech_array, turbulence_array = DP.df_to_array(data, if_vix)\n",
        "    env_config = {'price_array':price_array,\n",
        "              'tech_array':tech_array,\n",
        "              'turbulence_array':turbulence_array,\n",
        "              'if_train':True}\n",
        "    env_instance = env(config=env_config)\n",
        "\n",
        "    return env_instance\n",
        "\n",
        "def train(config=None):\n",
        "    with wandb.init(config=config, sync_tensorboard = True, save_code = True) as run:\n",
        "      #Get the training environment\n",
        "      train_env_instance = train_agent_env(TRAIN_START_DATE, TRAIN_END_DATE, ticker_list, data_source, time_interval, \n",
        "                            technical_indicator_list, env, model_name)\n",
        "      config = wandb.config\n",
        "      #Initialize the training agent\n",
        "      agent_train = DRLAgent_SB3(train_env_instance,run)\n",
        "      #For current set of hyperparameters initialize the model\n",
        "      model = agent_train.get_model(model_name, model_kwargs = config)\n",
        "      #train the model\n",
        "      trained_model = agent_train.train_model(model,total_timesteps)\n",
        "      run_ids[run.id] = run\n",
        "      print('Training finished!')\n",
        "      #Log the validation sharpe\n",
        "      sharpe,val_episode_total_asset = val_or_test(\n",
        "          VAL_START_DATE, VAL_END_DATE,run,ticker_list, \n",
        "          data_source, time_interval, \n",
        "          technical_indicator_list, env, model_name\n",
        "      )\n",
        "      #Log the testing sharpe\n",
        "      sharpe,val_episode_total_asset = val_or_test(\n",
        "          TEST_START_DATE, TEST_END_DATE,run,ticker_list, \n",
        "          data_source, time_interval, \n",
        "          technical_indicator_list, env, model_name,val_or_test = 'test'\n",
        "      )\n",
        "     "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIFpQvW44LxI"
      },
      "source": [
        "def val_or_test(start_date, end_date,run, ticker_list, data_source, time_interval, \n",
        "         technical_indicator_list, env, model_name,val_or_test='val', if_vix = True,\n",
        "         **kwargs):\n",
        "  \n",
        "  DP = DataProcessor(data_source, **kwargs)\n",
        "  data = DP.download_data(ticker_list, start_date, end_date, time_interval)\n",
        "  data = DP.clean_data(data)\n",
        "  data = DP.add_technical_indicator(data, technical_indicator_list)\n",
        "  \n",
        "  if if_vix:\n",
        "      data = DP.add_vix(data)\n",
        "  # if val_or_test == 'val':\n",
        "  #   data.to_csv('val.csv')\n",
        "  # elif val_or_test == 'test':\n",
        "  #   data.to_csv('test.csv')\n",
        "  # if val_or_test == 'val':\n",
        "  #   data=pd.read_csv('val.csv')\n",
        "  # elif val_or_test == 'test':\n",
        "  #   data = pd.read_csv('test.csv')\n",
        "  price_array, tech_array, turbulence_array = DP.df_to_array(data, if_vix)\n",
        "    \n",
        "  test_env_config = {'price_array':price_array,\n",
        "          'tech_array':tech_array,\n",
        "          'turbulence_array':turbulence_array,\n",
        "          'if_train':False}\n",
        "  env_instance = env(config=test_env_config)\n",
        "  \n",
        "  run_ids[run.id] = run\n",
        "  sharpe,episode_total_assets = DRLAgent_SB3.DRL_prediction_load_from_file(run,model_name,env_instance,val_or_test)\n",
        "  \n",
        "  return sharpe, episode_total_assets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsjYYCpdyLxP"
      },
      "source": [
        "TRAIN_START_DATE = '2009-01-01'\n",
        "TRAIN_END_DATE = '2019-07-30'\n",
        "\n",
        "VAL_START_DATE = '2019-08-01'\n",
        "VAL_END_DATE = '2020-07-30'\n",
        "\n",
        "TEST_START_DATE = '2020-08-01'\n",
        "TEST_END_DATE = '2021-10-01'\n",
        "\n",
        "# ticker_list = config.DOW_30_TICKER\n",
        "ticker_list = ['TSLA']\n",
        "data_source = 'yahoofinance'\n",
        "time_interval = '1D'\n",
        "technical_indicator_list = config.TECHNICAL_INDICATORS_LIST\n",
        "env = StockTradingEnv_numpy\n",
        "model_name = \"a2c\"\n",
        "\n",
        "# PPO_PARAMS = {\n",
        "#     \"n_steps\": 2048,\n",
        "#     \"ent_coef\": 0.01,\n",
        "#     \"learning_rate\": 0.00025,\n",
        "#     \"batch_size\": 128,\n",
        "# }\n",
        "\n",
        "total_timesteps = 15000\n",
        "run_ids = {}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        },
        "id": "E4XBN4a6nSlV",
        "outputId": "be424205-1990-4e4b-eca3-68f19fb15315"
      },
      "source": [
        "count = 30\n",
        "os.environ['WANDB_AGENT_MAX_INITIAL_FAILURES']= str(count-5)\n",
        "project_name = 'finrl-sweeps-sb3'\n",
        "sweep_config = model_params(model_name)\n",
        "\n",
        "sweep_id = wandb.sweep(sweep_config,project=project_name)\n",
        "wandb.agent(sweep_id, train, count=count)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Create sweep with ID: 42k4cl09\n",
            "Sweep URL: https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: d6k0p091 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.0680830685067986\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.7624511337158364\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/d6k0p091\" target=\"_blank\">lively-sweep-1</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.0680830685067986, 'learning_rate': 1.7624511337158364, 'n_steps': 256}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/d6k0p091/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/d6k0p091\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/d6k0p091\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for d6k0p091 is 0.925820099701886\n"
          ]
        },
        {
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">lively-sweep-1</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/d6k0p091\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/d6k0p091</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_062819-d6k0p091/logs</code><br/>\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: uulqsxeg with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.592796086131605\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.3063913463827002\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
          ]
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        {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/uulqsxeg\" target=\"_blank\">kind-sweep-2</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.592796086131605, 'learning_rate': 1.3063913463827002, 'n_steps': 1024}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/uulqsxeg/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/uulqsxeg\n",
            "episode_return 1.175419260636902\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/uulqsxeg\n",
            "episode_return 1.0977917389853524\n",
            "Test Finished!\n",
            "Test Sharpe for uulqsxeg is 0.42885680303634444\n"
          ]
        },
        {
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.42886</td></tr><tr><td>Val sharpe</td><td>1.48178</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">kind-sweep-2</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/uulqsxeg\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/uulqsxeg</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_062856-uulqsxeg/logs</code><br/>\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: k1xfgs1r with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.5528855317823398\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.2204403308455514\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/k1xfgs1r\" target=\"_blank\">colorful-sweep-3</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.5528855317823398, 'learning_rate': 2.2204403308455514, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/k1xfgs1r/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/k1xfgs1r\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/k1xfgs1r\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for k1xfgs1r is 0.925820099701886\n"
          ]
        },
        {
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            "text/html": [
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">colorful-sweep-3</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/k1xfgs1r\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/k1xfgs1r</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_062931-k1xfgs1r/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: kv4udn6y with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.680418000500025\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1848410027887395\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
          ]
        },
        {
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          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/kv4udn6y\" target=\"_blank\">effortless-sweep-4</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.680418000500025, 'learning_rate': 1.1848410027887395, 'n_steps': 1024}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/kv4udn6y/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/kv4udn6y\n",
            "episode_return 1.4536992687294619\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/kv4udn6y\n",
            "episode_return 1.5670930887495125\n",
            "Test Finished!\n",
            "Test Sharpe for kv4udn6y is 2.109050710228052\n"
          ]
        },
        {
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>2.10905</td></tr><tr><td>Val sharpe</td><td>2.5237</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">effortless-sweep-4</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/kv4udn6y\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/kv4udn6y</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063006-kv4udn6y/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: wr76po7p with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.555367543987813\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.8089074276498815\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
          ]
        },
        {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wr76po7p\" target=\"_blank\">drawn-sweep-5</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.555367543987813, 'learning_rate': 1.8089074276498815, 'n_steps': 2048}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/wr76po7p/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/wr76po7p\n",
            "episode_return 1.1267393826244965\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/wr76po7p\n",
            "episode_return 1.108737380340577\n",
            "Test Finished!\n",
            "Test Sharpe for wr76po7p is 0.7214148276465508\n"
          ]
        },
        {
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.72141</td></tr><tr><td>Val sharpe</td><td>2.23205</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">drawn-sweep-5</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wr76po7p\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wr76po7p</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063040-wr76po7p/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: obtgf58h with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1539481371743208\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.106896619772797\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
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          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/obtgf58h\" target=\"_blank\">winter-sweep-6</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.1539481371743208, 'learning_rate': 1.106896619772797, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/obtgf58h/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/obtgf58h\n",
            "episode_return 1.4000965289012908\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/obtgf58h\n",
            "episode_return 1.4722806108878177\n",
            "Test Finished!\n",
            "Test Sharpe for obtgf58h is 1.0614156663637666\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4024... <strong style=\"color:green\">(success).</strong>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.06142</td></tr><tr><td>Val sharpe</td><td>2.25642</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">winter-sweep-6</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/obtgf58h\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/obtgf58h</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063116-obtgf58h/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: r8ujjgos with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.714521374788854\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.647272310466235\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/r8ujjgos\" target=\"_blank\">valiant-sweep-7</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.714521374788854, 'learning_rate': 1.647272310466235, 'n_steps': 256}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/r8ujjgos/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/r8ujjgos\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/r8ujjgos\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for r8ujjgos is 0.925820099701886\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4066... <strong style=\"color:green\">(success).</strong>"
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              "VBox(children=(Label(value=' 0.09MB of 0.09MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">valiant-sweep-7</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/r8ujjgos\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/r8ujjgos</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063151-r8ujjgos/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: n1rvtmot with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.3683118174676627\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.4511744755130742\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/n1rvtmot\" target=\"_blank\">wobbly-sweep-8</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.3683118174676627, 'learning_rate': 1.4511744755130742, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/n1rvtmot/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/n1rvtmot\n",
            "episode_return 1.1890107450615388\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/n1rvtmot\n",
            "episode_return 1.2711865001982436\n",
            "Test Finished!\n",
            "Test Sharpe for n1rvtmot is 0.720157155690128\n"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.72016</td></tr><tr><td>Val sharpe</td><td>2.36003</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">wobbly-sweep-8</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/n1rvtmot\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/n1rvtmot</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063226-n1rvtmot/logs</code><br/>\n"
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: h391w2a6 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4596968541163566\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.4283952127241726\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/h391w2a6\" target=\"_blank\">ruby-sweep-9</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.4596968541163566, 'learning_rate': 1.4283952127241726, 'n_steps': 256}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/h391w2a6/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/h391w2a6\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/h391w2a6\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for h391w2a6 is 0.925820099701886\n"
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        {
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              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">ruby-sweep-9</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/h391w2a6\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/h391w2a6</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063301-h391w2a6/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: m8hbmnum with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.6474774199571893\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.2696227814414336\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
          ]
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/m8hbmnum\" target=\"_blank\">clean-sweep-10</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.6474774199571893, 'learning_rate': 1.2696227814414336, 'n_steps': 256}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/m8hbmnum/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/m8hbmnum\n",
            "episode_return 1.31715923807193\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/m8hbmnum\n",
            "episode_return 1.5247671784166255\n",
            "Test Finished!\n",
            "Test Sharpe for m8hbmnum is 1.8925636607724503\n"
          ]
        },
        {
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.89256</td></tr><tr><td>Val sharpe</td><td>2.82449</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">clean-sweep-10</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/m8hbmnum\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/m8hbmnum</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063339-m8hbmnum/logs</code><br/>\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ebw53hvk with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4043968559592843\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.5249858664154248\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 128\n"
          ]
        },
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ebw53hvk\" target=\"_blank\">quiet-sweep-11</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.4043968559592843, 'learning_rate': 1.5249858664154248, 'n_steps': 128}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/ebw53hvk/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------\n",
            "| rollout/              |          |\n",
            "|    ep_len_mean        | 2.66e+03 |\n",
            "|    ep_rew_mean        | 2.13e+19 |\n",
            "| time/                 |          |\n",
            "|    fps                | 941      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 12800    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | nan      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 1.52     |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | nan      |\n",
            "|    std                | nan      |\n",
            "|    value_loss         | inf      |\n",
            "------------------------------------\n"
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "</div><div class=\"wandb-col\">\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">quiet-sweep-11</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ebw53hvk\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ebw53hvk</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063415-ebw53hvk/logs</code><br/>\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Run ebw53hvk errored: RuntimeError('max must be larger than min')\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run ebw53hvk errored: RuntimeError('max must be larger than min')\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: irveookq with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.7388238992538436\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.0429915240310186\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
          ]
        },
        {
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          "data": {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/irveookq\" target=\"_blank\">grateful-sweep-12</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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        {
          "output_type": "stream",
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.7388238992538436, 'learning_rate': 1.0429915240310186, 'n_steps': 2048}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/irveookq/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/irveookq\n",
            "episode_return 1.3137513995652619\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/irveookq\n",
            "episode_return 1.3599589491005863\n",
            "Test Finished!\n",
            "Test Sharpe for irveookq is 1.3828317064241575\n"
          ]
        },
        {
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.38283</td></tr><tr><td>Val sharpe</td><td>2.11653</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">grateful-sweep-12</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/irveookq\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/irveookq</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063447-irveookq/logs</code><br/>\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 22kf7i8n with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.3456891896292897\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.637169195293926\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 128\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/22kf7i8n\" target=\"_blank\">atomic-sweep-13</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.3456891896292897, 'learning_rate': 1.637169195293926, 'n_steps': 128}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/22kf7i8n/A2C_1\n",
            "------------------------------------\n",
            "| rollout/              |          |\n",
            "|    ep_len_mean        | 2.66e+03 |\n",
            "|    ep_rew_mean        | 7.8      |\n",
            "| time/                 |          |\n",
            "|    fps                | 944      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 12800    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 1.64     |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 2.82e+03 |\n",
            "|    std                | 3.29e+17 |\n",
            "|    value_loss         | 4.58e+03 |\n",
            "------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/22kf7i8n\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/22kf7i8n\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for 22kf7i8n is 0.925820099701886\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4315... <strong style=\"color:green\">(success).</strong>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">atomic-sweep-13</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/22kf7i8n\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/22kf7i8n</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063523-22kf7i8n/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 3t2g1r3i with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.100929311395701\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.0843986994159707\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/3t2g1r3i\" target=\"_blank\">jolly-sweep-14</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.100929311395701, 'learning_rate': 2.0843986994159707, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/3t2g1r3i/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/3t2g1r3i\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/3t2g1r3i\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for 3t2g1r3i is 0.925820099701886\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4357... <strong style=\"color:green\">(success).</strong>"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">jolly-sweep-14</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/3t2g1r3i\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/3t2g1r3i</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063601-3t2g1r3i/logs</code><br/>\n"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ahd1cgmt with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4110973782937886\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.3122935362465777\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
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          "data": {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ahd1cgmt\" target=\"_blank\">laced-sweep-15</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.4110973782937886, 'learning_rate': 2.3122935362465777, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/ahd1cgmt/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/ahd1cgmt\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/ahd1cgmt\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for ahd1cgmt is 0.925820099701886\n"
          ]
        },
        {
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">laced-sweep-15</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ahd1cgmt\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/ahd1cgmt</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063639-ahd1cgmt/logs</code><br/>\n"
            ],
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: e4elr92m with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4689368730742487\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.6707893022090616\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
          ]
        },
        {
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          "data": {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/e4elr92m\" target=\"_blank\">spring-sweep-16</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.4689368730742487, 'learning_rate': 1.6707893022090616, 'n_steps': 256}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/e4elr92m/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/e4elr92m\n",
            "episode_return 1.1912216806622162\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/e4elr92m\n",
            "episode_return 1.0908810706260985\n",
            "Test Finished!\n",
            "Test Sharpe for e4elr92m is 0.3884245803001356\n"
          ]
        },
        {
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.38842</td></tr><tr><td>Val sharpe</td><td>2.98324</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">spring-sweep-16</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/e4elr92m\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/e4elr92m</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063716-e4elr92m/logs</code><br/>\n"
            ],
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              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: nilww7y9 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.9105193862252536\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1663353991825394\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/nilww7y9\" target=\"_blank\">divine-sweep-17</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.9105193862252536, 'learning_rate': 1.1663353991825394, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/nilww7y9/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/nilww7y9\n",
            "episode_return 1.2195270964392702\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/nilww7y9\n",
            "episode_return 1.054341791409302\n",
            "Test Finished!\n",
            "Test Sharpe for nilww7y9 is 0.4086356182663752\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.40864</td></tr><tr><td>Val sharpe</td><td>1.4876</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">divine-sweep-17</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/nilww7y9\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/nilww7y9</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063753-nilww7y9/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: gx4ebfj3 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.1751544966141045\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.9958384878083903\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/gx4ebfj3\" target=\"_blank\">worldly-sweep-18</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.1751544966141045, 'learning_rate': 1.9958384878083903, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/gx4ebfj3/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/gx4ebfj3\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/gx4ebfj3\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for gx4ebfj3 is 0.925820099701886\n"
          ]
        },
        {
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">worldly-sweep-18</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/gx4ebfj3\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/gx4ebfj3</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063831-gx4ebfj3/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: w7l02m63 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.3479839841134198\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.6399681124458274\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
          ]
        },
        {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/w7l02m63\" target=\"_blank\">whole-sweep-19</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.3479839841134198, 'learning_rate': 2.6399681124458274, 'n_steps': 1024}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/w7l02m63/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/w7l02m63\n",
            "episode_return 1.2075574225904084\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/w7l02m63\n",
            "episode_return 1.1309732481742563\n",
            "Test Finished!\n",
            "Test Sharpe for w7l02m63 is 0.5392738817752225\n"
          ]
        },
        {
          "output_type": "display_data",
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.53927</td></tr><tr><td>Val sharpe</td><td>1.86315</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">whole-sweep-19</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/w7l02m63\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/w7l02m63</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063909-w7l02m63/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: rkzpx1zr with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 2.54571917294826\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.435555434860724\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/rkzpx1zr\" target=\"_blank\">misunderstood-sweep-20</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 2.54571917294826, 'learning_rate': 2.435555434860724, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/rkzpx1zr/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/rkzpx1zr\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/rkzpx1zr\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for rkzpx1zr is 0.925820099701886\n"
          ]
        },
        {
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          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4609... <strong style=\"color:green\">(success).</strong>"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">misunderstood-sweep-20</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/rkzpx1zr\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/rkzpx1zr</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_063948-rkzpx1zr/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: b6io51xv with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.784650216926926\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.388070228611561\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/b6io51xv\" target=\"_blank\">usual-sweep-21</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.784650216926926, 'learning_rate': 2.388070228611561, 'n_steps': 256}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/b6io51xv/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/b6io51xv\n",
            "episode_return 1.2451970509100647\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/b6io51xv\n",
            "episode_return 1.1530098104300546\n",
            "Test Finished!\n",
            "Test Sharpe for b6io51xv is 0.6345106439739926\n"
          ]
        },
        {
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.63451</td></tr><tr><td>Val sharpe</td><td>1.89508</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">usual-sweep-21</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/b6io51xv\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/b6io51xv</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064027-b6io51xv/logs</code><br/>\n"
            ],
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              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 95movh84 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1487190557422124\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1842672218926504\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 256\n"
          ]
        },
        {
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          "data": {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/95movh84\" target=\"_blank\">fanciful-sweep-22</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.1487190557422124, 'learning_rate': 1.1842672218926504, 'n_steps': 256}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/95movh84/A2C_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:205: RuntimeWarning: overflow encountered in multiply\n",
            "  x = um.multiply(x, x, out=x)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: overflow encountered in reduce\n",
            "  ret = umr_sum(x, axis, dtype, out, keepdims)\n",
            "/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/utils.py:62: RuntimeWarning: invalid value encountered in float_scalars\n",
            "  return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training finished!\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/95movh84\n",
            "episode_return 4.1645433772305466e+17\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/95movh84\n",
            "episode_return 4.2908019102982746e+17\n",
            "Test Finished!\n",
            "Test Sharpe for 95movh84 is 0.925820099701886\n"
          ]
        },
        {
          "output_type": "display_data",
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              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.92582</td></tr><tr><td>Val sharpe</td><td>1.00199</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">fanciful-sweep-22</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/95movh84\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/95movh84</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064107-95movh84/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: fo4uvsfe with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.747718041201096\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.906454593305404\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/fo4uvsfe\" target=\"_blank\">devoted-sweep-23</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
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          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.747718041201096, 'learning_rate': 1.906454593305404, 'n_steps': 1024}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/fo4uvsfe/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/fo4uvsfe\n",
            "episode_return 1.06645602606041\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/fo4uvsfe\n",
            "episode_return 1.2786464211846933\n",
            "Test Finished!\n",
            "Test Sharpe for fo4uvsfe is 0.954514518401848\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4737... <strong style=\"color:green\">(success).</strong>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.95451</td></tr><tr><td>Val sharpe</td><td>1.74877</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">devoted-sweep-23</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/fo4uvsfe\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/fo4uvsfe</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064146-fo4uvsfe/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: xg4qvh2v with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.4124498071227023\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.6900827700006895\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/xg4qvh2v\" target=\"_blank\">splendid-sweep-24</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.4124498071227023, 'learning_rate': 1.6900827700006895, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/xg4qvh2v/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/xg4qvh2v\n",
            "episode_return 1.4809686025350037\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/xg4qvh2v\n",
            "episode_return 0.8188236306659559\n",
            "Test Finished!\n",
            "Test Sharpe for xg4qvh2v is -0.3445229678429466\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4779... <strong style=\"color:green\">(success).</strong>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>-0.34452</td></tr><tr><td>Val sharpe</td><td>1.87655</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">splendid-sweep-24</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/xg4qvh2v\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/xg4qvh2v</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064225-xg4qvh2v/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 074q8zou with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.354068684372759\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.1097771673485315\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/074q8zou\" target=\"_blank\">prime-sweep-25</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.354068684372759, 'learning_rate': 1.1097771673485315, 'n_steps': 512}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/074q8zou/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/074q8zou\n",
            "episode_return 1.2860597584413758\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/074q8zou\n",
            "episode_return 1.6458448378614507\n",
            "Test Finished!\n",
            "Test Sharpe for 074q8zou is 1.1636404033265162\n"
          ]
        },
        {
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.16364</td></tr><tr><td>Val sharpe</td><td>2.18409</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">prime-sweep-25</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/074q8zou\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/074q8zou</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064305-074q8zou/logs</code><br/>\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: iysz1zof with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.9710319973218224\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.51243520363503\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
          ]
        },
        {
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/iysz1zof\" target=\"_blank\">gallant-sweep-26</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.9710319973218224, 'learning_rate': 1.51243520363503, 'n_steps': 2048}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/iysz1zof/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/iysz1zof\n",
            "episode_return 1.6279849086196136\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/iysz1zof\n",
            "episode_return 1.3341774651092537\n",
            "Test Finished!\n",
            "Test Sharpe for iysz1zof is 1.0582408267367305\n"
          ]
        },
        {
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              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.05824</td></tr><tr><td>Val sharpe</td><td>2.15381</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">gallant-sweep-26</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/iysz1zof\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/iysz1zof</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064345-iysz1zof/logs</code><br/>\n"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 2rkl49f3 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1557908480149603\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.9271095041152968\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/2rkl49f3\" target=\"_blank\">prime-sweep-27</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.1557908480149603, 'learning_rate': 1.9271095041152968, 'n_steps': 2048}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/2rkl49f3/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/2rkl49f3\n",
            "episode_return 1.1000670991662294\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/2rkl49f3\n",
            "episode_return 1.4165930196394045\n",
            "Test Finished!\n",
            "Test Sharpe for 2rkl49f3 is 1.6525688540701402\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4905... <strong style=\"color:green\">(success).</strong>"
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        {
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          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.65257</td></tr><tr><td>Val sharpe</td><td>2.51438</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">prime-sweep-27</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/2rkl49f3\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/2rkl49f3</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064426-2rkl49f3/logs</code><br/>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: wtthdypt with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.054289661882152\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.4043717402884397\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 2048\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wtthdypt\" target=\"_blank\">likely-sweep-28</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
              "\n",
              "                "
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.054289661882152, 'learning_rate': 2.4043717402884397, 'n_steps': 2048}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/wtthdypt/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/wtthdypt\n",
            "episode_return 1.0\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/wtthdypt\n",
            "episode_return 1.0\n",
            "Test Finished!\n",
            "Test Sharpe for wtthdypt is 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 4955... <strong style=\"color:green\">(success).</strong>"
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        {
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          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0</td></tr><tr><td>Val sharpe</td><td>0</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">likely-sweep-28</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wtthdypt\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/wtthdypt</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064506-wtthdypt/logs</code><br/>\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: efnqijxw with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.1729056189153095\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.7969705879156763\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/efnqijxw\" target=\"_blank\">olive-sweep-29</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.1729056189153095, 'learning_rate': 1.7969705879156763, 'n_steps': 1024}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/efnqijxw/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/efnqijxw\n",
            "episode_return 1.1242879886185457\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/efnqijxw\n",
            "episode_return 0.9946450312878422\n",
            "Test Finished!\n",
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              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>0.08792</td></tr><tr><td>Val sharpe</td><td>1.39588</td></tr></table>\n",
              "</div></div>\n",
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)\n",
              "<br/>Synced <strong style=\"color:#cdcd00\">olive-sweep-29</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/efnqijxw\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/efnqijxw</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064546-efnqijxw/logs</code><br/>\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 23lw1vgl with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tent_coef: 1.02643407071229\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.3586418187270546\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tn_steps: 1024\n"
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/23lw1vgl\" target=\"_blank\">fanciful-sweep-30</a></strong> to <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "Sweep page: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/sweeps/42k4cl09</a><br/>\n",
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            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2286, 9)\n",
            "Clean data for TSLA\n",
            "NaN data on start date, fill using first valid data.\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2660, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "{'ent_coef': 1.02643407071229, 'learning_rate': 1.3586418187270546, 'n_steps': 1024}\n",
            "Using cuda device\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "Logging to runs/23lw1vgl/A2C_1\n",
            "Training finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/23lw1vgl\n",
            "episode_return 1.1871999987727662\n",
            "Test Finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for TSLA\n",
            "Data clean for TSLA is finished.\n",
            "Data clean all finished!\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (294, 9)\n",
            "Clean data for ^VIX\n",
            "Data clean for ^VIX is finished.\n",
            "Data clean all finished!\n",
            "['TSLA']\n",
            "Successfully transformed into array\n",
            "Successfully load model models/23lw1vgl\n",
            "episode_return 1.2579962087612313\n",
            "Test Finished!\n",
            "Test Sharpe for 23lw1vgl is 1.3300820176118635\n"
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              "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>▁</td></tr><tr><td>Val sharpe</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
              "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Test sharpe</td><td>1.33008</td></tr><tr><td>Val sharpe</td><td>1.83607</td></tr></table>\n",
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              "<br/>Synced <strong style=\"color:#cdcd00\">fanciful-sweep-30</strong>: <a href=\"https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/23lw1vgl\" target=\"_blank\">https://wandb.ai/athe_kunal/finrl-sweeps-sb3/runs/23lw1vgl</a><br/>\n",
              "Find logs at: <code>./wandb/run-20211105_064626-23lw1vgl/logs</code><br/>\n"
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