diff --git a/examples/FinRL_PaperTrading_Demo_refactored.py b/examples/FinRL_PaperTrading_Demo_refactored.py
new file mode 100644
index 000000000..40af3d16c
--- /dev/null
+++ b/examples/FinRL_PaperTrading_Demo_refactored.py
@@ -0,0 +1,199 @@
+# Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Many platforms exist for simulated trading (paper trading) which can be used for building and developing the methods discussed. Please use common sense and always first consult a professional before trading or investing.
+# install finrl library
+# %pip install --upgrade git+https://github.com/AI4Finance-Foundation/FinRL.git
+# Alpaca keys
+from __future__ import annotations
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("data_key", help="data source api key")
+parser.add_argument("data_secret", help="data source api secret")
+parser.add_argument("data_url", help="data source api base url")
+parser.add_argument("trading_key", help="trading api key")
+parser.add_argument("trading_secret", help="trading api secret")
+parser.add_argument("trading_url", help="trading api base url")
+args = parser.parse_args()
+DATA_API_KEY = args.data_key
+DATA_API_SECRET = args.data_secret
+DATA_API_BASE_URL = args.data_url
+TRADING_API_KEY = args.trading_key
+TRADING_API_SECRET = args.trading_secret
+TRADING_API_BASE_URL = args.trading_url
+
+print("DATA_API_KEY: ", DATA_API_KEY)
+print("DATA_API_SECRET: ", DATA_API_SECRET)
+print("DATA_API_BASE_URL: ", DATA_API_BASE_URL)
+print("TRADING_API_KEY: ", TRADING_API_KEY)
+print("TRADING_API_SECRET: ", TRADING_API_SECRET)
+print("TRADING_API_BASE_URL: ", TRADING_API_BASE_URL)
+
+from finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv
+from finrl.meta.paper_trading.alpaca import PaperTradingAlpaca
+from finrl.meta.paper_trading.common import train, test, alpaca_history, DIA_history
+from finrl.config import INDICATORS
+
+# Import Dow Jones 30 Symbols
+from finrl.config_tickers import DOW_30_TICKER
+
+ticker_list = DOW_30_TICKER
+env = StockTradingEnv
+# if you want to use larger datasets (change to longer period), and it raises error, please try to increase "target_step". It should be larger than the episode steps.
+ERL_PARAMS = {
+    "learning_rate": 3e-6,
+    "batch_size": 2048,
+    "gamma": 0.985,
+    "seed": 312,
+    "net_dimension": [128, 64],
+    "target_step": 5000,
+    "eval_gap": 30,
+    "eval_times": 1,
+}
+
+# Set up sliding window of 6 days training and 2 days testing
+import datetime
+from pandas.tseries.offsets import BDay  # BDay is business day, not birthday...
+
+today = datetime.datetime.today()
+
+TEST_END_DATE = (today - BDay(1)).to_pydatetime().date()
+TEST_START_DATE = (TEST_END_DATE - BDay(1)).to_pydatetime().date()
+TRAIN_END_DATE = (TEST_START_DATE - BDay(1)).to_pydatetime().date()
+TRAIN_START_DATE = (TRAIN_END_DATE - BDay(5)).to_pydatetime().date()
+TRAINFULL_START_DATE = TRAIN_START_DATE
+TRAINFULL_END_DATE = TEST_END_DATE
+
+TRAIN_START_DATE = str(TRAIN_START_DATE)
+TRAIN_END_DATE = str(TRAIN_END_DATE)
+TEST_START_DATE = str(TEST_START_DATE)
+TEST_END_DATE = str(TEST_END_DATE)
+TRAINFULL_START_DATE = str(TRAINFULL_START_DATE)
+TRAINFULL_END_DATE = str(TRAINFULL_END_DATE)
+
+print("TRAIN_START_DATE: ", TRAIN_START_DATE)
+print("TRAIN_END_DATE: ", TRAIN_END_DATE)
+print("TEST_START_DATE: ", TEST_START_DATE)
+print("TEST_END_DATE: ", TEST_END_DATE)
+print("TRAINFULL_START_DATE: ", TRAINFULL_START_DATE)
+print("TRAINFULL_END_DATE: ", TRAINFULL_END_DATE)
+
+train(
+    start_date=TRAIN_START_DATE,
+    end_date=TRAIN_END_DATE,
+    ticker_list=ticker_list,
+    data_source="alpaca",
+    time_interval="1Min",
+    technical_indicator_list=INDICATORS,
+    drl_lib="elegantrl",
+    env=env,
+    model_name="ppo",
+    if_vix=True,
+    API_KEY=DATA_API_KEY,
+    API_SECRET=DATA_API_SECRET,
+    API_BASE_URL=DATA_API_BASE_URL,
+    erl_params=ERL_PARAMS,
+    cwd="./papertrading_erl",  # current_working_dir
+    break_step=1e5,
+)
+
+account_value_erl = test(
+    start_date=TEST_START_DATE,
+    end_date=TEST_END_DATE,
+    ticker_list=ticker_list,
+    data_source="alpaca",
+    time_interval="1Min",
+    technical_indicator_list=INDICATORS,
+    drl_lib="elegantrl",
+    env=env,
+    model_name="ppo",
+    if_vix=True,
+    API_KEY=DATA_API_KEY,
+    API_SECRET=DATA_API_SECRET,
+    API_BASE_URL=DATA_API_BASE_URL,
+    cwd="./papertrading_erl",
+    net_dimension=ERL_PARAMS["net_dimension"],
+)
+
+train(
+    start_date=TRAINFULL_START_DATE,  # After tuning well, retrain on the training and testing sets
+    end_date=TRAINFULL_END_DATE,
+    ticker_list=ticker_list,
+    data_source="alpaca",
+    time_interval="1Min",
+    technical_indicator_list=INDICATORS,
+    drl_lib="elegantrl",
+    env=env,
+    model_name="ppo",
+    if_vix=True,
+    API_KEY=DATA_API_KEY,
+    API_SECRET=DATA_API_SECRET,
+    API_BASE_URL=DATA_API_BASE_URL,
+    erl_params=ERL_PARAMS,
+    cwd="./papertrading_erl_retrain",
+    break_step=2e5,
+)
+
+action_dim = len(DOW_30_TICKER)
+state_dim = (
+    1 + 2 + 3 * action_dim + len(INDICATORS) * action_dim
+)  # Calculate the DRL state dimension manually for paper trading. amount + (turbulence, turbulence_bool) + (price, shares, cd (holding time)) * stock_dim + tech_dim
+
+paper_trading_erl = PaperTradingAlpaca(
+    ticker_list=DOW_30_TICKER,
+    time_interval="1Min",
+    drl_lib="elegantrl",
+    agent="ppo",
+    cwd="./papertrading_erl_retrain",
+    net_dim=ERL_PARAMS["net_dimension"],
+    state_dim=state_dim,
+    action_dim=action_dim,
+    API_KEY=TRADING_API_KEY,
+    API_SECRET=TRADING_API_SECRET,
+    API_BASE_URL=TRADING_API_BASE_URL,
+    tech_indicator_list=INDICATORS,
+    turbulence_thresh=30,
+    max_stock=1e2,
+)
+
+paper_trading_erl.run()
+
+# Check Portfolio Performance
+# ## Get cumulative return
+df_erl, cumu_erl = alpaca_history(
+    key=DATA_API_KEY,
+    secret=DATA_API_SECRET,
+    url=DATA_API_BASE_URL,
+    start="2022-09-01",  # must be within 1 month
+    end="2022-09-12",
+)  # change the date if error occurs
+
+df_djia, cumu_djia = DIA_history(start="2022-09-01")
+returns_erl = cumu_erl - 1
+returns_dia = cumu_djia - 1
+returns_dia = returns_dia[: returns_erl.shape[0]]
+
+# plot and save
+import matplotlib.pyplot as plt
+
+plt.figure(dpi=1000)
+plt.grid()
+plt.grid(which="minor", axis="y")
+plt.title("Stock Trading (Paper trading)", fontsize=20)
+plt.plot(returns_erl, label="ElegantRL Agent", color="red")
+# plt.plot(returns_sb3, label = 'Stable-Baselines3 Agent', color = 'blue' )
+# plt.plot(returns_rllib, label = 'RLlib Agent', color = 'green')
+plt.plot(returns_dia, label="DJIA", color="grey")
+plt.ylabel("Return", fontsize=16)
+plt.xlabel("Year 2021", fontsize=16)
+plt.xticks(size=14)
+plt.yticks(size=14)
+ax = plt.gca()
+ax.xaxis.set_major_locator(ticker.MultipleLocator(78))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(6))
+ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.005))
+ax.yaxis.set_major_formatter(ticker.PercentFormatter(xmax=1, decimals=2))
+ax.xaxis.set_major_formatter(
+    ticker.FixedFormatter(["", "10-19", "", "10-20", "", "10-21", "", "10-22"])
+)
+plt.legend(fontsize=10.5)
+plt.savefig("papertrading_stock.png")
diff --git a/examples/Stock_NeurIPS2018_1_Data.ipynb b/examples/Stock_NeurIPS2018_1_Data.ipynb
index a189e29c3..9712f35f6 100644
--- a/examples/Stock_NeurIPS2018_1_Data.ipynb
+++ b/examples/Stock_NeurIPS2018_1_Data.ipynb
@@ -37,7 +37,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 2,
       "metadata": {
         "id": "j37flV31OJGW"
       },
@@ -384,1048 +384,297 @@
           "height": 206
         },
         "id": "_TgEjXxhXtT_",
-        "outputId": "a8e8a9e2-a1ea-472e-eddf-2227e6c901d8"
-      },
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date</th>\n",
-              "      <th>open</th>\n",
-              "      <th>high</th>\n",
-              "      <th>low</th>\n",
-              "      <th>close</th>\n",
-              "      <th>volume</th>\n",
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-              "      <td>2020-01-02</td>\n",
-              "      <td>74.059998</td>\n",
-              "      <td>75.150002</td>\n",
-              "      <td>73.797501</td>\n",
-              "      <td>73.449394</td>\n",
-              "      <td>135480400</td>\n",
-              "      <td>aapl</td>\n",
-              "      <td>3</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2020-01-03</td>\n",
-              "      <td>74.287498</td>\n",
-              "      <td>75.144997</td>\n",
-              "      <td>74.125000</td>\n",
-              "      <td>72.735313</td>\n",
-              "      <td>146322800</td>\n",
-              "      <td>aapl</td>\n",
-              "      <td>4</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2020-01-06</td>\n",
-              "      <td>73.447502</td>\n",
-              "      <td>74.989998</td>\n",
-              "      <td>73.187500</td>\n",
-              "      <td>73.314888</td>\n",
-              "      <td>118387200</td>\n",
-              "      <td>aapl</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2020-01-07</td>\n",
-              "      <td>74.959999</td>\n",
-              "      <td>75.224998</td>\n",
-              "      <td>74.370003</td>\n",
-              "      <td>72.970078</td>\n",
-              "      <td>108872000</td>\n",
-              "      <td>aapl</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2020-01-08</td>\n",
-              "      <td>74.290001</td>\n",
-              "      <td>76.110001</td>\n",
-              "      <td>74.290001</td>\n",
-              "      <td>74.143906</td>\n",
-              "      <td>132079200</td>\n",
-              "      <td>aapl</td>\n",
-              "      <td>2</td>\n",
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-            "text/plain": [
-              "         date       open       high        low      close     volume   tic  \\\n",
-              "0  2020-01-02  74.059998  75.150002  73.797501  73.449394  135480400  aapl   \n",
-              "1  2020-01-03  74.287498  75.144997  74.125000  72.735313  146322800  aapl   \n",
-              "2  2020-01-06  73.447502  74.989998  73.187500  73.314888  118387200  aapl   \n",
-              "3  2020-01-07  74.959999  75.224998  74.370003  72.970078  108872000  aapl   \n",
-              "4  2020-01-08  74.290001  76.110001  74.290001  74.143906  132079200  aapl   \n",
-              "\n",
-              "   day  \n",
-              "0    3  \n",
-              "1    4  \n",
-              "2    0  \n",
-              "3    1  \n",
-              "4    2  "
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "aapl_df_finrl.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9kcOE5nbic6R"
-      },
-      "source": [
-        "## Data for the chosen tickers"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FKBjtAo2uIq5",
-        "outputId": "927f682a-9cc3-4c11-c3f1-094ae811af6b"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "631"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(config_tickers.DOW_30_TICKER+config_tickers.NAS_100_TICKER+config_tickers.SP_500_TICKER)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "9xTPG4Fhc-zL"
-      },
-      "outputs": [],
-      "source": [
-        "TRAIN_START_DATE = '2000-01-01'\n",
-        "TRAIN_END_DATE = '2024-01-01'\n",
-        "TRADE_START_DATE = '2023-09-01'\n",
-        "TRADE_END_DATE = '2024-09-01'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "9LblMI8CO0F3",
-        "outputId": "7be76385-50eb-4e8d-f2e5-1795d77b70ba"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
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-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['XLNX']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CTXS']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['NLOK']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ATVI']: YFPricesMissingError('$%ticker%: possibly delisted; no price data found  (1d 2000-01-01 -> 2024-09-01) (Yahoo error = \"No data found, symbol may be delisted\")')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CERN']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['MYL']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ALXN']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['FB']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['MXIM']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['WLTW']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ABC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ABMD']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ADS']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['AGN']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ALXN']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ANTM']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ARNC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ATVI']: YFPricesMissingError('$%ticker%: possibly delisted; no price data found  (1d 2000-01-01 -> 2024-09-01) (Yahoo error = \"No data found, symbol may be delisted\")')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['BBT']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['BF.B']: YFPricesMissingError('$%ticker%: possibly delisted; no price data found  (1d 2000-01-01 -> 2024-09-01)')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['BHGE']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['BLL']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['BRK.B']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CBS']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CELG']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CERN']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['COG']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CTL']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CTXS']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['CXO']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['DISCK']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['DISH']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['DRE']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['ETFC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['FB']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['FBHS']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['FLIR']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['FLT']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['FRC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['HFC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['INFO']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['JEC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['KSU']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['MXIM']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['MYL']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['NBL']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['NLSN']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['PBCT']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['PKI']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['PXD']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['RE']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['RTN']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['SIVB']: YFPricesMissingError('$%ticker%: possibly delisted; no price data found  (1d 2000-01-01 -> 2024-09-01)')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['SYMC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['TIF']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['TSS']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['TWTR']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['UTX']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['VAR']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['VIAB']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n",
+        "outputId": "a8e8a9e2-a1ea-472e-eddf-2227e6c901d8"
+      },
+      "outputs": [
+        {
+          "data": {
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+              "<table border=\"1\" class=\"dataframe\">\n",
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+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>open</th>\n",
+              "      <th>high</th>\n",
+              "      <th>low</th>\n",
+              "      <th>close</th>\n",
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+              "      <th>0</th>\n",
+              "      <td>2020-01-02</td>\n",
+              "      <td>74.059998</td>\n",
+              "      <td>75.150002</td>\n",
+              "      <td>73.797501</td>\n",
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+              "      <th>1</th>\n",
+              "      <td>2020-01-03</td>\n",
+              "      <td>74.287498</td>\n",
+              "      <td>75.144997</td>\n",
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+              "      <th>2</th>\n",
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+              "      <th>3</th>\n",
+              "      <td>2020-01-07</td>\n",
+              "      <td>74.959999</td>\n",
+              "      <td>75.224998</td>\n",
+              "      <td>74.370003</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-01-08</td>\n",
+              "      <td>74.290001</td>\n",
+              "      <td>76.110001</td>\n",
+              "      <td>74.290001</td>\n",
+              "      <td>74.143906</td>\n",
+              "      <td>132079200</td>\n",
+              "      <td>aapl</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99616e29-275e-48e3-81dd-9dcbcbc33fe9')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-99616e29-275e-48e3-81dd-9dcbcbc33fe9 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-99616e29-275e-48e3-81dd-9dcbcbc33fe9');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "         date       open       high        low      close     volume   tic  \\\n",
+              "0  2020-01-02  74.059998  75.150002  73.797501  73.449394  135480400  aapl   \n",
+              "1  2020-01-03  74.287498  75.144997  74.125000  72.735313  146322800  aapl   \n",
+              "2  2020-01-06  73.447502  74.989998  73.187500  73.314888  118387200  aapl   \n",
+              "3  2020-01-07  74.959999  75.224998  74.370003  72.970078  108872000  aapl   \n",
+              "4  2020-01-08  74.290001  76.110001  74.290001  74.143906  132079200  aapl   \n",
+              "\n",
+              "   day  \n",
+              "0    3  \n",
+              "1    4  \n",
+              "2    0  \n",
+              "3    1  \n",
+              "4    2  "
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "aapl_df_finrl.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9kcOE5nbic6R"
+      },
+      "source": [
+        "## Data for the chosen tickers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FKBjtAo2uIq5",
+        "outputId": "927f682a-9cc3-4c11-c3f1-094ae811af6b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['AXP',\n",
+              " 'AMGN',\n",
+              " 'AAPL',\n",
+              " 'BA',\n",
+              " 'CAT',\n",
+              " 'CSCO',\n",
+              " 'CVX',\n",
+              " 'GS',\n",
+              " 'HD',\n",
+              " 'HON',\n",
+              " 'IBM',\n",
+              " 'INTC',\n",
+              " 'JNJ',\n",
+              " 'KO',\n",
+              " 'JPM',\n",
+              " 'MCD',\n",
+              " 'MMM',\n",
+              " 'MRK',\n",
+              " 'MSFT',\n",
+              " 'NKE',\n",
+              " 'PG',\n",
+              " 'TRV',\n",
+              " 'UNH',\n",
+              " 'CRM',\n",
+              " 'VZ',\n",
+              " 'V',\n",
+              " 'WBA',\n",
+              " 'WMT',\n",
+              " 'DIS',\n",
+              " 'DOW']"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "config_tickers.DOW_30_TICKER"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "9xTPG4Fhc-zL"
+      },
+      "outputs": [],
+      "source": [
+        "TRAIN_START_DATE = '2009-01-01'\n",
+        "TRAIN_END_DATE = '2020-07-01'\n",
+        "TRADE_START_DATE = '2020-07-01'\n",
+        "TRADE_END_DATE = '2021-10-29'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9LblMI8CO0F3",
+        "outputId": "7be76385-50eb-4e8d-f2e5-1795d77b70ba"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
@@ -1433,37 +682,22 @@
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['WCG']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['WLTW']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['WRK']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['XEC']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
-            "\n",
-            "1 Failed download:\n",
-            "['XLNX']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
@@ -1471,35 +705,14 @@
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
             "[*********************100%***********************]  1 of 1 completed\n",
-            "[*********************100%***********************]  1 of 1 completed\n"
-          ]
-        },
-        {
-          "ename": "MemoryError",
-          "evalue": "Unable to allocate 49.1 MiB for an array with shape (3219026,) and data type complex128",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
-            "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m df_raw \u001b[38;5;241m=\u001b[39m \u001b[43mYahooDownloader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_date\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mTRAIN_START_DATE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mend_date\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mTRADE_END_DATE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m----> 3\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mticker_list\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_tickers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDOW_30_TICKER\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mconfig_tickers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNAS_100_TICKER\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mconfig_tickers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSP_500_TICKER\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32mD:\\workstation\\quant\\FinRL\\finrl\\meta\\preprocessor\\yahoodownloader.py:85\u001b[0m, in \u001b[0;36mYahooDownloader.fetch_data\u001b[1;34m(self, proxy)\u001b[0m\n\u001b[0;32m     83\u001b[0m data_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mday\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m data_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39mdayofweek\n\u001b[0;32m     84\u001b[0m \u001b[38;5;66;03m# convert date to standard string format, easy to filter\u001b[39;00m\n\u001b[1;32m---> 85\u001b[0m data_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdata_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrftime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm-\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     86\u001b[0m \u001b[38;5;66;03m# drop missing data\u001b[39;00m\n\u001b[0;32m     87\u001b[0m data_df \u001b[38;5;241m=\u001b[39m data_df\u001b[38;5;241m.\u001b[39mdropna()\n",
-            "File \u001b[1;32md:\\Users\\Steven Chen\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\series.py:4924\u001b[0m, in \u001b[0;36mSeries.apply\u001b[1;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[0;32m   4789\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[0;32m   4790\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   4791\u001b[0m     func: AggFuncType,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4796\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   4797\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[0;32m   4798\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   4799\u001b[0m \u001b[38;5;124;03m    Invoke function on values of Series.\u001b[39;00m\n\u001b[0;32m   4800\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4915\u001b[0m \u001b[38;5;124;03m    dtype: float64\u001b[39;00m\n\u001b[0;32m   4916\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m   4917\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   4918\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4919\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4920\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4921\u001b[0m \u001b[43m        \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4922\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4923\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m-> 4924\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32md:\\Users\\Steven Chen\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\apply.py:1427\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1424\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[0;32m   1426\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[1;32m-> 1427\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32md:\\Users\\Steven Chen\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\apply.py:1507\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1501\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[0;32m   1502\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[0;32m   1503\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[0;32m   1504\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[0;32m   1505\u001b[0m \u001b[38;5;66;03m#  Categorical (GH51645).\u001b[39;00m\n\u001b[0;32m   1506\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1507\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1508\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[0;32m   1509\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1511\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[0;32m   1512\u001b[0m     \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[0;32m   1513\u001b[0m     \u001b[38;5;66;03m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[0;32m   1514\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n",
-            "File \u001b[1;32md:\\Users\\Steven Chen\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\base.py:919\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[1;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[0;32m    916\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\n\u001b[0;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m--> 919\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m algorithms\u001b[38;5;241m.\u001b[39mmap_array(arr, mapper, na_action\u001b[38;5;241m=\u001b[39mna_action, convert\u001b[38;5;241m=\u001b[39mconvert)\n",
-            "File \u001b[1;32md:\\Users\\Steven Chen\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\arrays\\_mixins.py:81\u001b[0m, in \u001b[0;36mravel_compat.<locals>.method\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(meth)\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmethod\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m---> 81\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m meth(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     83\u001b[0m     flags \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ndarray\u001b[38;5;241m.\u001b[39mflags\n\u001b[0;32m     84\u001b[0m     flat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mravel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-            "File \u001b[1;32md:\\Users\\Steven Chen\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\arrays\\datetimelike.py:740\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin.map\u001b[1;34m(self, mapper, na_action)\u001b[0m\n\u001b[0;32m    736\u001b[0m \u001b[38;5;129m@ravel_compat\u001b[39m\n\u001b[0;32m    737\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmap\u001b[39m(\u001b[38;5;28mself\u001b[39m, mapper, na_action\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    738\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Index\n\u001b[1;32m--> 740\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    741\u001b[0m     result \u001b[38;5;241m=\u001b[39m Index(result)\n\u001b[0;32m    743\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, ABCMultiIndex):\n",
-            "File \u001b[1;32md:\\Users\\Steven Chen\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\algorithms.py:1743\u001b[0m, in \u001b[0;36mmap_array\u001b[1;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[0;32m   1741\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1743\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1745\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[0;32m   1746\u001b[0m         values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[0;32m   1747\u001b[0m     )\n",
-            "File \u001b[1;32mlib.pyx:2981\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
-            "File \u001b[1;32mlib.pyx:2539\u001b[0m, in \u001b[0;36mpandas._libs.lib.maybe_convert_objects\u001b[1;34m()\u001b[0m\n",
-            "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 49.1 MiB for an array with shape (3219026,) and data type complex128"
+            "Shape of DataFrame:  (94301, 8)\n"
           ]
         }
       ],
       "source": [
         "df_raw = YahooDownloader(start_date = TRAIN_START_DATE,\n",
         "                     end_date = TRADE_END_DATE,\n",
-        "                     ticker_list = config_tickers.DOW_30_TICKER+config_tickers.NAS_100_TICKER+config_tickers.SP_500_TICKER).fetch_data()"
+        "                     ticker_list = config_tickers.DOW_30_TICKER).fetch_data()"
       ]
     },
     {
@@ -2143,16 +1356,7 @@
       "name": "python3"
     },
     "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.8"
+      "name": "python"
     }
   },
   "nbformat": 4,
diff --git a/examples/Stock_NeurIPS2018_1_Data.py b/examples/Stock_NeurIPS2018_1_Data.py
deleted file mode 100644
index a87b26da6..000000000
--- a/examples/Stock_NeurIPS2018_1_Data.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from __future__ import annotations
-
-import datetime
-import itertools
-
-import numpy as np
-import pandas as pd
-import yfinance as yf
-
-from finrl import config_tickers
-from finrl.config import INDICATORS
-from finrl.meta.preprocessor.preprocessors import data_split
-from finrl.meta.preprocessor.preprocessors import FeatureEngineer
-from finrl.meta.preprocessor.yahoodownloader import YahooDownloader
-
-
-TRAIN_START_DATE = "2000-01-01"
-TRAIN_END_DATE = "2024-01-01"
-TRADE_START_DATE = "2023-01-01"
-TRADE_END_DATE = "2024-01-01"
-
-
-df_raw = YahooDownloader(
-    start_date=TRAIN_START_DATE,
-    end_date=TRADE_END_DATE,
-    ticker_list=config_tickers.DOW_30_TICKER
-    + config_tickers.NAS_100_TICKER
-    + config_tickers.SP_500_TICKER,
-).fetch_data()
-
-
-df_raw.head()
-
-
-fe = FeatureEngineer(
-    use_technical_indicator=True,
-    tech_indicator_list=INDICATORS,
-    use_vix=True,
-    use_turbulence=True,
-    user_defined_feature=False,
-)
-
-processed = fe.preprocess_data(df_raw)
-
-
-list_ticker = processed["tic"].unique().tolist()
-list_date = list(
-    pd.date_range(processed["date"].min(), processed["date"].max()).astype(str)
-)
-combination = list(itertools.product(list_date, list_ticker))
-
-processed_full = pd.DataFrame(combination, columns=["date", "tic"]).merge(
-    processed, on=["date", "tic"], how="left"
-)
-processed_full = processed_full[processed_full["date"].isin(processed["date"])]
-processed_full = processed_full.sort_values(["date", "tic"])
-
-processed_full = processed_full.fillna(0)
-
-
-processed_full.head()
-
-train = data_split(processed_full, TRAIN_START_DATE, TRAIN_END_DATE)
-trade = data_split(processed_full, TRADE_START_DATE, TRADE_END_DATE)
-print(len(train))
-print(len(trade))
-
-train.to_csv("train_data.csv")
-trade.to_csv("trade_data.csv")
diff --git a/examples/ensemble_stock_trading_metrics_analysis.py.ipynb b/examples/ensemble_stock_trading_metrics_analysis.py.ipynb
deleted file mode 100644
index 545f5719e..000000000
--- a/examples/ensemble_stock_trading_metrics_analysis.py.ipynb
+++ /dev/null
@@ -1,956 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from tqdm import tqdm\n",
-    "from stable_baselines3 import A2C, DDPG, PPO, SAC, TD3\n",
-    "\n",
-    "from finrl.agents.stablebaselines3.models import DRLAgent\n",
-    "from finrl.config import INDICATORS, TRAINED_MODEL_DIR\n",
-    "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
-    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
-    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trade = pd.read_csv('trade_data.csv')\n",
-    "trade = trade.set_index(trade.columns[0])\n",
-    "trade.index.names = ['']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>tic</th>\n",
-       "      <th>open</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>close</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>day</th>\n",
-       "      <th>macd</th>\n",
-       "      <th>boll_ub</th>\n",
-       "      <th>boll_lb</th>\n",
-       "      <th>rsi_30</th>\n",
-       "      <th>cci_30</th>\n",
-       "      <th>dx_30</th>\n",
-       "      <th>close_30_sma</th>\n",
-       "      <th>close_60_sma</th>\n",
-       "      <th>vix</th>\n",
-       "      <th>turbulence</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2023-01-03</td>\n",
-       "      <td>AAPL</td>\n",
-       "      <td>130.279999</td>\n",
-       "      <td>130.899994</td>\n",
-       "      <td>124.169998</td>\n",
-       "      <td>123.904625</td>\n",
-       "      <td>112117500.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-4.664941</td>\n",
-       "      <td>148.380015</td>\n",
-       "      <td>121.517494</td>\n",
-       "      <td>39.011489</td>\n",
-       "      <td>-130.926001</td>\n",
-       "      <td>35.987249</td>\n",
-       "      <td>138.780054</td>\n",
-       "      <td>141.061399</td>\n",
-       "      <td>22.9</td>\n",
-       "      <td>58.114417</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2023-01-03</td>\n",
-       "      <td>ADBE</td>\n",
-       "      <td>340.160004</td>\n",
-       "      <td>345.820007</td>\n",
-       "      <td>331.920013</td>\n",
-       "      <td>336.920013</td>\n",
-       "      <td>2229100.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.018147</td>\n",
-       "      <td>344.361914</td>\n",
-       "      <td>325.710086</td>\n",
-       "      <td>51.312759</td>\n",
-       "      <td>55.832999</td>\n",
-       "      <td>17.420819</td>\n",
-       "      <td>334.678332</td>\n",
-       "      <td>321.834165</td>\n",
-       "      <td>22.9</td>\n",
-       "      <td>58.114417</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2023-01-03</td>\n",
-       "      <td>ADI</td>\n",
-       "      <td>165.570007</td>\n",
-       "      <td>166.199997</td>\n",
-       "      <td>161.440002</td>\n",
-       "      <td>157.418777</td>\n",
-       "      <td>4475700.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.279274</td>\n",
-       "      <td>168.515870</td>\n",
-       "      <td>154.019671</td>\n",
-       "      <td>51.525939</td>\n",
-       "      <td>-68.722035</td>\n",
-       "      <td>8.072027</td>\n",
-       "      <td>161.102222</td>\n",
-       "      <td>151.353293</td>\n",
-       "      <td>22.9</td>\n",
-       "      <td>58.114417</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2023-01-03</td>\n",
-       "      <td>ADP</td>\n",
-       "      <td>240.789993</td>\n",
-       "      <td>241.509995</td>\n",
-       "      <td>235.270004</td>\n",
-       "      <td>228.564453</td>\n",
-       "      <td>1749800.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-3.817562</td>\n",
-       "      <td>258.519213</td>\n",
-       "      <td>221.412165</td>\n",
-       "      <td>45.483461</td>\n",
-       "      <td>-115.839651</td>\n",
-       "      <td>14.036269</td>\n",
-       "      <td>243.547140</td>\n",
-       "      <td>235.814659</td>\n",
-       "      <td>22.9</td>\n",
-       "      <td>58.114417</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2023-01-03</td>\n",
-       "      <td>ADSK</td>\n",
-       "      <td>190.619995</td>\n",
-       "      <td>192.960007</td>\n",
-       "      <td>183.000000</td>\n",
-       "      <td>185.149994</td>\n",
-       "      <td>1181600.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-4.526453</td>\n",
-       "      <td>203.341768</td>\n",
-       "      <td>180.661229</td>\n",
-       "      <td>44.659114</td>\n",
-       "      <td>-94.290116</td>\n",
-       "      <td>3.860456</td>\n",
-       "      <td>195.468332</td>\n",
-       "      <td>200.417166</td>\n",
-       "      <td>22.9</td>\n",
-       "      <td>58.114417</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         date   tic        open        high         low       close  \\\n",
-       "0  2023-01-03  AAPL  130.279999  130.899994  124.169998  123.904625   \n",
-       "0  2023-01-03  ADBE  340.160004  345.820007  331.920013  336.920013   \n",
-       "0  2023-01-03   ADI  165.570007  166.199997  161.440002  157.418777   \n",
-       "0  2023-01-03   ADP  240.789993  241.509995  235.270004  228.564453   \n",
-       "0  2023-01-03  ADSK  190.619995  192.960007  183.000000  185.149994   \n",
-       "\n",
-       "        volume  day      macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
-       "0  112117500.0  1.0 -4.664941  148.380015  121.517494  39.011489 -130.926001   \n",
-       "0    2229100.0  1.0  2.018147  344.361914  325.710086  51.312759   55.832999   \n",
-       "0    4475700.0  1.0  0.279274  168.515870  154.019671  51.525939  -68.722035   \n",
-       "0    1749800.0  1.0 -3.817562  258.519213  221.412165  45.483461 -115.839651   \n",
-       "0    1181600.0  1.0 -4.526453  203.341768  180.661229  44.659114  -94.290116   \n",
-       "\n",
-       "       dx_30  close_30_sma  close_60_sma   vix  turbulence  \n",
-       "0  35.987249    138.780054    141.061399  22.9   58.114417  \n",
-       "0  17.420819    334.678332    321.834165  22.9   58.114417  \n",
-       "0   8.072027    161.102222    151.353293  22.9   58.114417  \n",
-       "0  14.036269    243.547140    235.814659  22.9   58.114417  \n",
-       "0   3.860456    195.468332    200.417166  22.9   58.114417  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trade=data_split(trade, '2023-01-01', '2024-01-01')\n",
-    "trade.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stock Dimension: 68, State Space: 681\n"
-     ]
-    }
-   ],
-   "source": [
-    "stock_dimension = len(trade.tic.unique())\n",
-    "state_space = 1 + 2 * stock_dimension + len(INDICATORS) * stock_dimension\n",
-    "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n",
-    "\n",
-    "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
-    "num_stock_shares = [0] * stock_dimension\n",
-    "\n",
-    "env_kwargs = {\n",
-    "    \"hmax\": 100,\n",
-    "    \"initial_amount\": 1000000,\n",
-    "    \"num_stock_shares\": num_stock_shares,\n",
-    "    \"buy_cost_pct\": buy_cost_list,\n",
-    "    \"sell_cost_pct\": sell_cost_list,\n",
-    "    \"state_space\": state_space,\n",
-    "    \"stock_dim\": stock_dimension,\n",
-    "    \"tech_indicator_list\": INDICATORS,\n",
-    "    \"action_space\": stock_dimension,\n",
-    "    \"reward_scaling\": 1e-4\n",
-    "}\n",
-    "\n",
-    "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 70,risk_indicator_col='vix', **env_kwargs)\n",
-    "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trained_a2c = A2C.load(TRAINED_MODEL_DIR + \"/agent_a2c\")\n",
-    "trained_ddpg = DDPG.load(TRAINED_MODEL_DIR + \"/agent_ddpg\")\n",
-    "trained_ppo = PPO.load(TRAINED_MODEL_DIR + \"/agent_ppo\")\n",
-    "trained_td3 = TD3.load(TRAINED_MODEL_DIR + \"/agent_td3\")\n",
-    "trained_sac = SAC.load(TRAINED_MODEL_DIR + \"/agent_sac\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def DRL_ensemble_prediction(environment, a2c_model=None, ddpg_model=None, ppo_model=None, td3_model=None, sac_model=None, weights=[1,1,1,1,1], deterministic=True):\n",
-    "    test_env, test_obs = environment.get_sb_env()\n",
-    "    account_memory = None\n",
-    "    actions_memory = None\n",
-    "\n",
-    "\n",
-    "    test_env.reset()\n",
-    "    max_steps = len(environment.df.index.unique()) - 1\n",
-    "\n",
-    "    print(max_steps)\n",
-    "\n",
-    "    _weights = []\n",
-    "    if a2c_model is not None:\n",
-    "        _weights.append(weights[0])\n",
-    "    if ddpg_model is not None:\n",
-    "        _weights.append(weights[1])\n",
-    "    if ppo_model is not None:\n",
-    "        _weights.append(weights[2])\n",
-    "    if td3_model is not None:\n",
-    "        _weights.append(weights[3])\n",
-    "    if sac_model is not None:\n",
-    "        _weights.append(weights[4])\n",
-    "    \n",
-    "    weights = np.array(_weights)\n",
-    "    \n",
-    "\n",
-    "    for i in tqdm(range(len(environment.df.index.unique()))):\n",
-    "        if a2c_model is not None:\n",
-    "            a2c_action, _states = a2c_model.predict(test_obs, deterministic=deterministic)\n",
-    "        if ddpg_model is not None:\n",
-    "            ddpg_action, _states = ddpg_model.predict(test_obs, deterministic=deterministic)\n",
-    "        if ppo_model is not None:\n",
-    "            ppo_action, _states = ppo_model.predict(test_obs, deterministic=deterministic)\n",
-    "        if td3_model is not None:\n",
-    "            td3_action, _states = td3_model.predict(test_obs, deterministic=deterministic)\n",
-    "        if sac_model is not None:\n",
-    "           sac_action, _states = sac_model.predict(test_obs, deterministic=deterministic)\n",
-    "        \n",
-    "\n",
-    "        actions = []\n",
-    "        action_length = 0\n",
-    "\n",
-    "        if a2c_model is not None and len(a2c_action) > 0:\n",
-    "            action_length = len(a2c_action[0])\n",
-    "        elif ddpg_model is not None and len(ddpg_action) > 0:\n",
-    "            action_length = len(ddpg_action[0])\n",
-    "        elif ppo_model is not None and len(ppo_action) > 0:\n",
-    "            action_length = len(ppo_action[0])\n",
-    "        elif td3_model is not None and len(td3_action) > 0:\n",
-    "            action_length = len(td3_action[0])\n",
-    "        elif sac_model is not None and len(sac_action) > 0:\n",
-    "            action_length = len(sac_action[0])\n",
-    "\n",
-    "        for j in range(action_length):\n",
-    "            _actions = []\n",
-    "            if a2c_model is not None:\n",
-    "                # print(a2c_action)\n",
-    "                _actions.append(a2c_action[0][j])\n",
-    "            if ddpg_model is not None:\n",
-    "                _actions.append(ddpg_action[0][j])\n",
-    "            if ppo_model is not None:\n",
-    "                _actions.append(ppo_action[0][j])\n",
-    "            if td3_model is not None:\n",
-    "                _actions.append(td3_action[0][j])\n",
-    "            if sac_model is not None:\n",
-    "                _actions.append(sac_action[0][j])\n",
-    "            _action = np.sum(_actions * weights) / np.sum(weights)\n",
-    "            actions.append(_action)\n",
-    "        actions[0] = np.array(actions)\n",
-    "\n",
-    "        test_obs, rewards, dones, info = test_env.step(actions)  \n",
-    "\n",
-    "        if (i == max_steps - 1):  \n",
-    "            account_memory = test_env.env_method(method_name=\"save_asset_memory\")\n",
-    "            actions_memory = test_env.env_method(method_name=\"save_action_memory\")\n",
-    "\n",
-    "\n",
-    "\n",
-    "        if dones[0]:  \n",
-    "            print(\"hit end!\")\n",
-    "            break\n",
-    "\n",
-    "    return account_memory[0], actions_memory[0]  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "248\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████▉| 248/249 [00:01<00:00, 172.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hit end!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_account_value_merge, df_actions_merge = DRL_ensemble_prediction(\n",
-    "    a2c_model=trained_a2c, \n",
-    "    ddpg_model=trained_ddpg,\n",
-    "    ppo_model=trained_ppo,\n",
-    "    td3_model=trained_td3,\n",
-    "    sac_model=trained_sac,\n",
-    "    weights=[1, 2, 3, 4, 5],\n",
-    "    environment = e_trade_gym)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>account_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>239</th>\n",
-       "      <td>2023-12-14</td>\n",
-       "      <td>1.336939e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>240</th>\n",
-       "      <td>2023-12-15</td>\n",
-       "      <td>1.343660e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>241</th>\n",
-       "      <td>2023-12-18</td>\n",
-       "      <td>1.344525e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>2023-12-19</td>\n",
-       "      <td>1.355349e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>2023-12-20</td>\n",
-       "      <td>1.334816e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244</th>\n",
-       "      <td>2023-12-21</td>\n",
-       "      <td>1.349302e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>245</th>\n",
-       "      <td>2023-12-22</td>\n",
-       "      <td>1.354918e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>246</th>\n",
-       "      <td>2023-12-26</td>\n",
-       "      <td>1.363353e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>2023-12-27</td>\n",
-       "      <td>1.363219e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
-       "      <td>2023-12-28</td>\n",
-       "      <td>1.367380e+06</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           date  account_value\n",
-       "239  2023-12-14   1.336939e+06\n",
-       "240  2023-12-15   1.343660e+06\n",
-       "241  2023-12-18   1.344525e+06\n",
-       "242  2023-12-19   1.355349e+06\n",
-       "243  2023-12-20   1.334816e+06\n",
-       "244  2023-12-21   1.349302e+06\n",
-       "245  2023-12-22   1.354918e+06\n",
-       "246  2023-12-26   1.363353e+06\n",
-       "247  2023-12-27   1.363219e+06\n",
-       "248  2023-12-28   1.367380e+06"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_account_value_merge.tail(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_env, test_obs = e_trade_gym.get_sb_env()\n",
-    "a2c_action, _states = trained_a2c.predict(test_obs, deterministic=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.        ,  1.        ,  1.        ,  1.        , -1.        ,\n",
-       "        -1.        ,  1.        , -0.65387243,  1.        , -1.        ,\n",
-       "         1.        ,  1.        , -1.        , -1.        , -1.        ,\n",
-       "        -1.        , -1.        , -1.        ,  1.        ,  0.569773  ,\n",
-       "         0.45166317,  1.        , -1.        , -0.52100146, -1.        ,\n",
-       "         1.        , -1.        , -1.        ,  0.16547962, -1.        ,\n",
-       "        -1.        ,  1.        , -0.34704542,  0.02504051, -1.        ,\n",
-       "        -0.3966578 ,  1.        ,  0.14025176,  0.02251373,  1.        ,\n",
-       "         0.49129128,  0.20406397, -1.        , -0.596449  , -1.        ,\n",
-       "         0.24977826,  0.83777153,  1.        , -1.        , -1.        ,\n",
-       "        -1.        , -1.        ,  1.        ,  1.        , -1.        ,\n",
-       "        -1.        ,  0.17500924,  1.        , -0.01665697,  1.        ,\n",
-       "        -1.        , -1.        , -0.36138016,  1.        , -1.        ,\n",
-       "        -1.        ,  1.        , -1.        ]], dtype=float32)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a2c_action"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['AAPL', 'ADBE', 'ADI', 'ADP', 'ADSK', 'ALGN', 'AMAT', 'AMD',\n",
-       "       'AMGN', 'AMZN', 'ASML', 'BIIB', 'BKNG', 'BMRN', 'CDNS', 'CHKP',\n",
-       "       'CMCSA', 'COST', 'CSCO', 'CSX', 'CTAS', 'CTSH', 'DLTR', 'EA',\n",
-       "       'EBAY', 'FAST', 'GILD', 'HAS', 'HSIC', 'IDXX', 'ILMN', 'INCY',\n",
-       "       'INTC', 'INTU', 'ISRG', 'JBHT', 'KLAC', 'LRCX', 'MAR', 'MCHP',\n",
-       "       'MDLZ', 'MNST', 'MSFT', 'MU', 'NFLX', 'NTAP', 'NTES', 'NVDA',\n",
-       "       'ORLY', 'PAYX', 'PCAR', 'PEP', 'QCOM', 'REGN', 'ROST', 'SBUX',\n",
-       "       'SIRI', 'SNPS', 'SWKS', 'TCOM', 'TTWO', 'TXN', 'VRSN', 'VRTX',\n",
-       "       'WBA', 'WDC', 'WYNN', 'XEL'], dtype=object)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trade['tic'].unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# DJI index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TRADE_START_DATE = df_account_value_merge['date'].unique().tolist()[0]\n",
-    "TRADE_END_DATE = df_account_value_merge['date'].unique().tolist()[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1/1 [00:01<00:00,  1.79s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of DataFrame:  (248, 8)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_dji = YahooDownloader(\n",
-    "    start_date=TRADE_START_DATE, end_date=TRADE_END_DATE, ticker_list=[\"^DJI\"]\n",
-    ").fetch_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_dji = df_dji[[\"date\", \"close\"]]\n",
-    "fst_day = df_dji[\"close\"][0]\n",
-    "dji = pd.merge(\n",
-    "    df_dji[\"date\"],\n",
-    "    df_dji[\"close\"].div(fst_day).mul(1000000),\n",
-    "    how=\"outer\",\n",
-    "    left_index=True,\n",
-    "    right_index=True,\n",
-    ").set_index(\"date\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_result_merge = (df_account_value_merge.set_index(df_account_value_merge.columns[0]))\n",
-    "\n",
-    "result = pd.DataFrame(\n",
-    "    {\n",
-    "        \"merge strategy\": df_result_merge[\"account_value\"],\n",
-    "        # \"mvo\": MVO_result[\"Mean Var\"],\n",
-    "        \"dji\": dji[\"close\"],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 12000x4000 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.rcParams[\"figure.figsize\"] = (15,5)\n",
-    "plt.figure(dpi=800)\n",
-    "result.plot()\n",
-    "plt.grid(True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Calculate Indicators"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_rolling_sharpe(account_value, DJI_value, window=252):\n",
-    "    if len(account_value) != len(DJI_value):\n",
-    "        raise ValueError(\"The length of account_value and DJI_value must be the same.\")\n",
-    "    \n",
-    "    # Convert account and DJI values to returns\n",
-    "    account_returns = np.diff(account_value) / account_value[:-1]\n",
-    "    DJI_returns = np.diff(DJI_value) / DJI_value[:-1]\n",
-    "\n",
-    "    # Rolling Sharpe Ratio calculation\n",
-    "    sharpe_ratios = []\n",
-    "    for i in range(window+1):\n",
-    "        sharpe_ratios.append(0)\n",
-    "    for i in range(window, len(account_returns)):\n",
-    "        account_slice = account_returns[i-window:i]\n",
-    "        DJI_slice = DJI_returns[i-window:i]\n",
-    "\n",
-    "        # Excess returns (difference between account and DJI)\n",
-    "        excess_returns = account_slice - DJI_slice\n",
-    "\n",
-    "        # Calculate Sharpe Ratio: (mean of excess returns) / (std of excess returns)\n",
-    "        sharpe_ratio = np.mean(excess_returns) / np.std(excess_returns, ddof=1)\n",
-    "        sharpe_ratios.append(sharpe_ratio)\n",
-    "    \n",
-    "    return sharpe_ratios\n",
-    "\n",
-    "# Function to calculate Alpha\n",
-    "def calculate_alpha(account_value, DJI_value, risk_free_rate=0.01, window=252):\n",
-    "    if len(account_value) != len(DJI_value):\n",
-    "        raise ValueError(\"The length of account_value and DJI_value must be the same.\")\n",
-    "    \n",
-    "    account_returns = np.diff(account_value) / account_value[:-1]\n",
-    "    DJI_returns = np.diff(DJI_value) / DJI_value[:-1]\n",
-    "\n",
-    "    alphas = []\n",
-    "    for i in range(window+1):\n",
-    "        alphas.append(0)\n",
-    "    for i in range(window, len(account_returns)):\n",
-    "        account_slice = account_returns[i-window:i]\n",
-    "        DJI_slice = DJI_returns[i-window:i]\n",
-    "        \n",
-    "        # Calculate excess returns for both the account and DJI relative to risk-free rate\n",
-    "        excess_account = account_slice - risk_free_rate / 252\n",
-    "        excess_DJI = DJI_slice - risk_free_rate / 252\n",
-    "        \n",
-    "        # Alpha = (Mean of account returns - risk-free rate) - Beta * (Mean of DJI returns - risk-free rate)\n",
-    "        cov = np.cov(excess_account, excess_DJI)[0, 1]\n",
-    "        var = np.var(excess_DJI)\n",
-    "        beta = cov / var\n",
-    "        alpha = np.mean(excess_account) - beta * np.mean(excess_DJI)\n",
-    "        alphas.append(alpha)\n",
-    "    \n",
-    "    return alphas\n",
-    "\n",
-    "# Function to calculate Beta\n",
-    "def calculate_beta(account_value, DJI_value, window=252):\n",
-    "    if len(account_value) != len(DJI_value):\n",
-    "        raise ValueError(\"The length of account_value and DJI_value must be the same.\")\n",
-    "    \n",
-    "    account_returns = np.diff(account_value) / account_value[:-1]\n",
-    "    DJI_returns = np.diff(DJI_value) / DJI_value[:-1]\n",
-    "\n",
-    "    betas = []\n",
-    "    for i in range(window+1):\n",
-    "        betas.append(0)\n",
-    "    for i in range(window, len(account_returns)):\n",
-    "        account_slice = account_returns[i-window:i]\n",
-    "        DJI_slice = DJI_returns[i-window:i]\n",
-    "        \n",
-    "        # Beta = Cov(account, DJI) / Var(DJI)\n",
-    "        cov = np.cov(account_slice, DJI_slice)[0, 1]\n",
-    "        var = np.var(DJI_slice)\n",
-    "        beta = cov / var\n",
-    "        betas.append(beta)\n",
-    "    \n",
-    "    return betas\n",
-    "\n",
-    "# Function to calculate Sortino Ratio\n",
-    "def calculate_sortino_ratio(account_value, DJI_value, window=252, risk_free_rate=0.01):\n",
-    "    if len(account_value) != len(DJI_value):\n",
-    "        raise ValueError(\"The length of account_value and DJI_value must be the same.\")\n",
-    "    \n",
-    "    account_returns = np.diff(account_value) / account_value[:-1]\n",
-    "    DJI_returns = np.diff(DJI_value) / DJI_value[:-1]\n",
-    "\n",
-    "    sortino_ratios = []\n",
-    "    for i in range(window+1):\n",
-    "        sortino_ratios.append(0)\n",
-    "    for i in range(window, len(account_returns)):\n",
-    "        account_slice = account_returns[i-window:i]\n",
-    "        DJI_slice = DJI_returns[i-window:i]\n",
-    "        \n",
-    "        # Excess returns (difference between account and DJI returns)\n",
-    "        excess_returns = account_slice - DJI_slice\n",
-    "        downside_deviation = np.std([x for x in excess_returns if x < 0], ddof=1)\n",
-    "\n",
-    "        # Sortino Ratio = Mean of excess returns / Downside deviation\n",
-    "        sortino_ratio = np.mean(excess_returns) / downside_deviation if downside_deviation != 0 else np.nan\n",
-    "        sortino_ratios.append(sortino_ratio)\n",
-    "    \n",
-    "    return sortino_ratios\n",
-    "\n",
-    "# Function to calculate Volatility\n",
-    "def calculate_volatility(account_value, window=252):\n",
-    "    account_returns = np.diff(account_value) / account_value[:-1]\n",
-    "    \n",
-    "    volatilities = []\n",
-    "    for i in range(window+1):\n",
-    "        volatilities.append(0)\n",
-    "    for i in range(window, len(account_returns)):\n",
-    "        account_slice = account_returns[i-window:i]\n",
-    "        \n",
-    "        # Volatility = Standard deviation of returns\n",
-    "        volatility = np.std(account_slice, ddof=1)\n",
-    "        volatilities.append(volatility)\n",
-    "    \n",
-    "    return volatilities\n",
-    "\n",
-    "account_value = df_account_value_merge['account_value'].tolist()[0:-1]\n",
-    "dji_value = dji['close'].tolist()\n",
-    "\n",
-    "sharpe_ratio = calculate_rolling_sharpe(account_value, dji_value)\n",
-    "# print(f\"Sharpe Ratio: {sharpe_ratio:.4f}\")\n",
-    "# print(calculate_rolling_sharpe(account_value, dji_value))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account_value = df_account_value_merge['account_value'].tolist()[0:-1]\n",
-    "dji_value = df_dji['close'].tolist()\n",
-    "\n",
-    "window = 30\n",
-    "\n",
-    "sharpe_ratio = calculate_rolling_sharpe(account_value, dji_value, window=window)\n",
-    "alpha = calculate_alpha(account_value, dji_value, window=window)\n",
-    "beta = calculate_beta(account_value, dji_value, window=window)\n",
-    "sortino_ratio = calculate_sortino_ratio(account_value, dji_value, window=window)\n",
-    "volatility = calculate_volatility(account_value, window=window)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result2 = pd.DataFrame(\n",
-    "    {\n",
-    "        \"Sharpe Ratio\": sharpe_ratio,\n",
-    "        \"Beta\": beta,\n",
-    "        \"alpha\": alpha,\n",
-    "        \"Sortino Ratio\": sortino_ratio,\n",
-    "        \"Volatility\": volatility,\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1500x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "plt.rcParams[\"figure.figsize\"] = (15, 5)\n",
-    "\n",
-    "dates = pd.to_datetime(df_dji['date'])\n",
-    "\n",
-    "fig, ax1 = plt.subplots()\n",
-    "\n",
-    "ax1.set_xlabel('Date')\n",
-    "ax1.set_ylabel('Sharpe Ratio / Beta / Sortino Ratio', color='tab:blue')\n",
-    "ax1.plot(dates, result2['Sharpe Ratio'], label='Sharpe Ratio', color='tab:blue')\n",
-    "ax1.plot(dates, result2['Beta'], label='Beta', color='orange')\n",
-    "ax1.plot(dates, result2['Sortino Ratio'], label='Sortino Ratio', color='green')\n",
-    "ax1.tick_params(axis='y', labelcolor='tab:blue')\n",
-    "\n",
-    "ax2 = ax1.twinx()\n",
-    "ax2.set_ylabel('Alpha / Volatility', color='tab:red')\n",
-    "ax2.plot(dates, result2['alpha'], label='alpha', color='red')\n",
-    "ax2.plot(dates, result2['Volatility'], label='Volatility', color='purple')\n",
-    "ax2.tick_params(axis='y', labelcolor='tab:red')\n",
-    "\n",
-    "fig.tight_layout()\n",
-    "ax1.legend(loc='upper left')\n",
-    "ax2.legend(loc='upper right')\n",
-    "\n",
-    "ax1.xaxis.set_major_locator(mdates.AutoDateLocator())\n",
-    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
-    "\n",
-    "fig.autofmt_xdate()\n",
-    "\n",
-    "# 显示网格线和图形\n",
-    "plt.title('{} Day Performance Metrics'.format(window))\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/finrl/agents/stablebaselines3/models.py b/finrl/agents/stablebaselines3/models.py
index efeb25bdc..c4297310a 100644
--- a/finrl/agents/stablebaselines3/models.py
+++ b/finrl/agents/stablebaselines3/models.py
@@ -85,7 +85,6 @@ def get_model(
         verbose=1,
         seed=None,
         tensorboard_log=None,
-        device="cpu",
     ):
         if model_name not in MODELS:
             raise ValueError(
@@ -108,7 +107,6 @@ def get_model(
             verbose=verbose,
             policy_kwargs=policy_kwargs,
             seed=seed,
-            device=device,
             **model_kwargs,
         )
 
@@ -198,7 +196,6 @@ def get_model(
         model_kwargs=None,
         seed=None,
         verbose=1,
-        device="cpu",
     ):
         if model_name not in MODELS:
             raise ValueError(
@@ -223,7 +220,6 @@ def get_model(
             verbose=verbose,
             policy_kwargs=policy_kwargs,
             seed=seed,
-            device=device,
             **temp_model_kwargs,
         )
 
diff --git a/finrl/meta/preprocessor/yahoodownloader.py b/finrl/meta/preprocessor/yahoodownloader.py
index bb2c66ae8..cfefd7f4c 100644
--- a/finrl/meta/preprocessor/yahoodownloader.py
+++ b/finrl/meta/preprocessor/yahoodownloader.py
@@ -6,7 +6,6 @@
 
 import pandas as pd
 import yfinance as yf
-from tqdm import tqdm
 
 
 class YahooDownloader:
@@ -48,13 +47,9 @@ def fetch_data(self, proxy=None) -> pd.DataFrame:
         # Download and save the data in a pandas DataFrame:
         data_df = pd.DataFrame()
         num_failures = 0
-        for tic in tqdm(self.ticker_list):
+        for tic in self.ticker_list:
             temp_df = yf.download(
-                tic,
-                start=self.start_date,
-                end=self.end_date,
-                proxy=proxy,
-                progress=False,
+                tic, start=self.start_date, end=self.end_date, proxy=proxy
             )
             temp_df["tic"] = tic
             if len(temp_df) > 0: