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Fix processor bug and format

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Jactus
2020-11-11 14:24:04 +08:00
parent e2d89f44fb
commit 52c0c4b7a8
8 changed files with 114 additions and 101 deletions
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46
examples/workflow_by_code_xgboost.py
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@@ -21,7 +21,6 @@ from qlib.utils import init_instance_by_config
if __name__ == "__main__":
# use default data
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
if not exists_qlib_data(provider_uri):
@@ -36,15 +35,14 @@ if __name__ == "__main__":
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",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": MARKET,
}
@@ -62,43 +60,49 @@ if __name__ == "__main__":
"class": "XGBModel",
"module_path": "qlib.contrib.model.xgboost",
"kwargs": {
"objective": 'reg:linear',
"n_estimators":5000,
"objective": "reg:linear",
"n_estimators": 5000,
"colsample_bytree": 0.85,
"learning_rate": 0.0421,
"subsample": 0.8789,
"max_depth": 8,
"num_leaves": 210,
"num_threads": 20,
"missing":-1,
"min_child_weight":1,
"nthread":4,
"tree_method":'hist',
}
"missing": -1,
"min_child_weight": 1,
"nthread": 4,
"tree_method": "hist",
},
},
"dataset": {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
'handler': {
"handler": {
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": DATA_HANDLER_CONFIG
"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",),
}
}
"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 = train_model(task)
model = init_instance_by_config(task['model'])
dataset = init_instance_by_config(task['dataset'])
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
model.fit(dataset)
pred_score = model.predict(dataset)