` ###################################

train model

################################### data_handler_config = { "start_time": "2008-01-01", "end_time": "2020-08-01", "fit_start_time": "2015-01-01", "fit_end_time": "2018-12-31", "instruments": market, }

task = { "model": { "class": "LGBModel", "module_path": "qlib.contrib.model.gbdt", "kwargs": { "loss": "mse", "colsample_bytree": 0.8879, "learning_rate": 0.0421, "subsample": 0.8789, "lambda_l1": 205.6999, "lambda_l2": 580.9768, "max_depth": 8, "num_leaves": 210, "num_threads": 20, }, }, "dataset": { "class": "DatasetH", "module_path": "qlib.data.dataset", "kwargs": { "handler": { "class": "Alpha360", "module_path": "qlib.contrib.data.handler", "kwargs": data_handler_config, }, "segments": { "train": ("2008-01-01", "2014-12-31"), "valid": ("2015-01-01", "2015-12-31"), "test": ("2017-01-01", "2020-08-01"), }, }, }, }

model initiaiton

model = init_instance_by_config(task["model"]) dataset = init_instance_by_config(task["dataset"])

start exp to train model

with R.start(experiment_name="train_model"): R.log_params(**flatten_dict(task)) model.fit(dataset) R.save_objects(trained_model=model) rid = R.get_recorder().id

###################################

prediction, backtest & analysis

################################### port_analysis_config = { "executor": { "class": "SimulatorExecutor", "module_path": "qlib.backtest.executor", "kwargs": { "time_per_step": "day", "generate_portfolio_metrics": True, }, }, "strategy": { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.signal_strategy", "kwargs": { "model": model, "dataset": dataset, "topk": 50, "n_drop": 5, }, }, "backtest": { "start_time": "2015-01-01", "end_time": "2020-08-01", "account": 100000000, "benchmark": benchmark, "exchange_kwargs": { "freq": "day", "limit_threshold": 0.095, "deal_price": "close", "open_cost": 0.0005, "close_cost": 0.0015, "min_cost": 5, }, }, }

backtest and analysis

with R.start(experiment_name="backtest_analysis"): recorder = R.get_recorder(recorder_id=rid, experiment_name="train_model") model = recorder.load_object("trained_model")

import csv

feature_importance = model.get_feature_importance()
fea_expr, fea_name = dataset.handler.get_feature_config()
feature_importance = {fea_name[int(i.split("_")[1])]: v for i,v in feature_importance.items()}

with open('C:\\alpha360_feature.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=feature_importance.keys())
    writer.writeheader()
    writer.writerow(feature_importance)

# prediction
recorder = R.get_recorder()
ba_rid = recorder.id
sr = SignalRecord(model, dataset, recorder)
sr.generate()

# backtest & analysis
par = PortAnaRecord(recorder, port_analysis_config, "day")
par.generate()`

I only change class to Alpha360, but the backtest always throw exception as: