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trade_account support multi bar report

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bxdd
2021-04-29 02:15:34 +08:00
parent 8920c1967f
commit 86a6f565e8
15 changed files with 362 additions and 209 deletions
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58
qlib/workflow/record_temp.py
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@@ -233,8 +233,8 @@ class PortAnaRecord(SignalRecord):
super().__init__(recorder=recorder, **kwargs)
self.strategy_config = config["strategy"]
self.env_config = config["env"]
self.backtest_config = config["backtest"]
self.strategy = init_instance_by_config(self.strategy_config, accept_types=BaseStrategy)
def generate(self, **kwargs):
# check previously stored prediction results
@@ -244,36 +244,32 @@ class PortAnaRecord(SignalRecord):
super().generate()
# custom strategy and get backtest
pred_score = super().load("pred.pkl")
report_dict = normal_backtest(pred_score, strategy=self.strategy, **self.backtest_config)
report_normal = report_dict.get("report_df")
positions_normal = report_dict.get("positions")
self.recorder.save_objects(**{"report_normal.pkl": report_normal}, artifact_path=PortAnaRecord.get_path())
self.recorder.save_objects(**{"positions_normal.pkl": positions_normal}, artifact_path=PortAnaRecord.get_path())
order_normal = report_dict.get("order_list")
if order_normal:
self.recorder.save_objects(**{"order_normal.pkl": order_normal}, artifact_path=PortAnaRecord.get_path())
# analysis
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"]
)
# save portfolio analysis results
analysis_df = pd.concat(analysis) # type: pd.DataFrame
# log metrics
self.recorder.log_metrics(**flatten_dict(analysis_df["risk"].unstack().T.to_dict()))
# save results
self.recorder.save_objects(**{"port_analysis.pkl": analysis_df}, artifact_path=PortAnaRecord.get_path())
logger.info(
f"Portfolio analysis record 'port_analysis.pkl' has been saved as the artifact of the Experiment {self.recorder.experiment_id}"
)
# print out results
pprint("The following are analysis results of the excess return without cost.")
pprint(analysis["excess_return_without_cost"])
pprint("The following are analysis results of the excess return with cost.")
pprint(analysis["excess_return_with_cost"])
report_list = normal_backtest(env=self.env_config, strategy=self.strategy_config, **self.backtest_config)
for report_id, (report_normal, positions_normal) in enumerate(report_list):
if report_dict is None:
continue
self.recorder.save_objects(**{f"report_normal_{report_id}.pkl": report_normal}, artifact_path=PortAnaRecord.get_path())
self.recorder.save_objects(**{f"positions_norma_{report_id}l.pkl": positions_normal}, artifact_path=PortAnaRecord.get_path())
# analysis
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
# log metrics
self.recorder.log_metrics(**flatten_dict(analysis_df["risk"].unstack().T.to_dict()))
# save results
self.recorder.save_objects(**{f"port_analysis.pkl_{report_id}": analysis_df}, artifact_path=PortAnaRecord.get_path())
logger.info(
f"Portfolio analysis record 'port_analysis_{report_id}.pkl' has been saved as the artifact of the Experiment {self.recorder.experiment_id}"
)
# print out results
pprint("The following are analysis results of the excess return without cost.")
pprint(analysis["excess_return_without_cost"])
pprint("The following are analysis results of the excess return with cost.")
pprint(analysis["excess_return_with_cost"])
def list(self):
return [