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Update R and workflow

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This commit is contained in:
Jactus
2020-11-16 15:49:50 +08:00
parent c06914eb39
commit 42867264f3
13 changed files with 301 additions and 275 deletions
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81
examples/workflow_by_code.py
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@@ -14,10 +14,9 @@ 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
from qlib.utils import exists_qlib_data, init_instance_by_config
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
if __name__ == "__main__":
@@ -93,55 +92,41 @@ if __name__ == "__main__":
),
},
},
}
},
# You shoud record the data in specific sequence
# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
"record": ["SignalRecord", "PortAnaRecord"],
}
# model = train_model(task)
port_analysis_config = {
"strategy": {
"topk": 50,
"n_drop": 5,
},
"backtest": {
"verbose": False,
"limit_threshold": 0.095,
"account": 100000000,
"benchmark": BENCHMARK,
"deal_price": "close",
"open_cost": 0.0005,
"close_cost": 0.0015,
"min_cost": 5,
},
}
# model initiaiton
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
model.fit(dataset)
# start exp
with R.start("workflow"):
model.fit(dataset)
pred_score = model.predict(dataset)
# prediction
recorder = R.get_recorder()
sr = SignalRecord(model, dataset, recorder)
sr.generate()
# 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)
# backtest
par = PortAnaRecord(recorder, port_analysis_config)
par.generate()