diff --git a/examples/benchmarks/TFT/workflow_by_code_tft.py b/examples/benchmarks/TFT/workflow_by_code_tft.py
deleted file mode 100644
index 64c7d3df5..000000000
--- a/examples/benchmarks/TFT/workflow_by_code_tft.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import sys
-from pathlib import Path
-
-import qlib
-import pandas as pd
-from qlib.config import REG_CN
-from qlib.contrib.model.pytorch_lstm import LSTM
-from qlib.contrib.data.handler import ALPHA360_Denoise
-from qlib.contrib.strategy.strategy import TopkDropoutStrategy
-from qlib.contrib.evaluate import (
-    backtest as normal_backtest,
-    risk_analysis,
-)
-from qlib.utils import exists_qlib_data
-
-# from qlib.model.learner import train_model
-from qlib.utils import init_instance_by_config
-
-import pickle
-from tft import TFTModel
-
-if __name__ == "__main__":
-
-    # use default data
-    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
-    if not exists_qlib_data(provider_uri):
-        print(f"Qlib data is not found in {provider_uri}")
-        sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
-        from get_data import GetData
-
-        GetData().qlib_data_cn(target_dir=provider_uri)
-
-    qlib.init(provider_uri=provider_uri, region=REG_CN)
-
-    MARKET = "csi300"
-    BENCHMARK = "SH000300"
-
-    ###################################
-    # train model
-    ###################################
-    DATA_HANDLER_CONFIG = {
-        "start_time": "2008-01-01",
-        "end_time": "2020-08-01",
-        "fit_start_time": "2008-01-01",
-        "fit_end_time": "2014-12-31",
-        "instruments": MARKET,
-    }
-
-    TRAINER_CONFIG = {
-        "train_start_time": "2008-01-01",
-        "train_end_time": "2014-12-31",
-        "validate_start_time": "2015-01-01",
-        "validate_end_time": "2016-12-31",
-        "test_start_time": "2017-01-01",
-        "test_end_time": "2020-08-01",
-    }
-
-    task = {
-        "dataset": {
-            "class": "DatasetH",
-            "module_path": "qlib.data.dataset",
-            "kwargs": {
-                "handler": {
-                    "class": "Alpha158",
-                    "module_path": "qlib.contrib.data.handler",
-                    "kwargs": DATA_HANDLER_CONFIG,
-                },
-                "segments": {
-                    "train": ("2008-01-01", "2014-12-31"),
-                    "valid": (
-                        "2015-01-01",
-                        "2016-12-31",
-                    ),
-                    "test": (
-                        "2017-01-01",
-                        "2020-08-01",
-                    ),
-                },
-            },
-        }
-        # You shoud record the data in specific sequence
-        # "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
-    }
-
-    model = TFTModel()
-    dataset = init_instance_by_config(task["dataset"])
-    model.fit(dataset)
-
-    pred_score, label_score = model.predict(dataset)
-
-    # save pred_score to file
-    pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
-    pred_score_path.parent.mkdir(exist_ok=True, parents=True)
-    pred_score.to_pickle(pred_score_path)
-
-    ###################################
-    # backtest
-    ###################################
-    STRATEGY_CONFIG = {
-        "topk": 50,
-        "n_drop": 5,
-    }
-    BACKTEST_CONFIG = {
-        "verbose": False,
-        "limit_threshold": 0.095,
-        "account": 100000000,
-        "benchmark": BENCHMARK,
-        "deal_price": "close",
-        "open_cost": 0.0005,
-        "close_cost": 0.0015,
-        "min_cost": 5,
-    }
-
-    # use default strategy
-    # custom Strategy, refer to: TODO: Strategy API url
-    strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
-    report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
-
-    ###################################
-    # analyze
-    # If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
-    ###################################
-    analysis = dict()
-    analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
-    analysis["excess_return_with_cost"] = risk_analysis(
-        report_normal["return"] - report_normal["bench"] - report_normal["cost"]
-    )
-    analysis_df = pd.concat(analysis)  # type: pd.DataFrame
-    print(analysis_df)