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Update R and workflow

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This commit is contained in:
Jactus
2020-11-17 22:05:18 +08:00
parent a8b46dd41d
commit 64ed43b791
20 changed files with 481 additions and 376 deletions
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53
qlib/workflow/record_temp.py
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@@ -3,6 +3,7 @@
import pandas as pd
from pathlib import Path
from pprint import pprint
from ..contrib.evaluate import (
backtest as normal_backtest,
risk_analysis,
@@ -83,14 +84,14 @@ class SignalRecord(RecordTemp):
logger.info(
f"Signal record 'pred.pkl' has been saved as the artifact of the Experiment {self.recorder.experiment_id}"
)
# print out results
pprint(f"The following are prediction results of the {type(self.model).__name__} model.")
pprint(pred.head(5))
def load(self):
# try to load the saved object
try:
pred = self.recorder.load_object("pred.pkl")
return pred
except:
raise Exception("Something went wrong when loading the saved object.")
pred = self.recorder.load_object("pred.pkl")
return pred
def check(self, **kwargs):
artifacts = self.recorder.list_artifacts()
@@ -148,19 +149,51 @@ class PortAnaRecord(SignalRecord):
analysis["excess_return_with_cost"] = risk_analysis(
report_normal["return"] - report_normal["bench"] - report_normal["cost"]
)
# log metrics
self.recorder.log_metrics(
excess_return_without_cost_mean=analysis["excess_return_without_cost"]["risk"]["mean"]
)
self.recorder.log_metrics(excess_return_without_cost_std=analysis["excess_return_without_cost"]["risk"]["std"])
self.recorder.log_metrics(
excess_return_without_cost_annualized_return=analysis["excess_return_without_cost"]["risk"][
"annualized_return"
]
)
self.recorder.log_metrics(
excess_return_without_cost_information_ratio=analysis["excess_return_without_cost"]["risk"][
"information_ratio"
]
)
self.recorder.log_metrics(
excess_return_without_cost_max_drawdown=analysis["excess_return_without_cost"]["risk"]["max_drawdown"]
)
self.recorder.log_metrics(excess_return_with_cost_mean=analysis["excess_return_with_cost"]["risk"]["mean"])
self.recorder.log_metrics(excess_return_with_cost_std=analysis["excess_return_with_cost"]["risk"]["std"])
self.recorder.log_metrics(
excess_return_with_cost_annualized_return=analysis["excess_return_with_cost"]["risk"]["annualized_return"]
)
self.recorder.log_metrics(
excess_return_with_cost_information_ratio=analysis["excess_return_with_cost"]["risk"]["information_ratio"]
)
self.recorder.log_metrics(
excess_return_with_cost_max_drawdown=analysis["excess_return_with_cost"]["risk"]["max_drawdown"]
)
# save portfolio analysis results
analysis_df = pd.concat(analysis) # type: pd.DataFrame
self.recorder.save_objects(**{"port_analysis.pkl": analysis_df}, artifact_path=self.artifact_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"])
def load(self):
# try to load the saved object
try:
pred = self.recorder.load_object(self.artifact_path / "port_analysis.pkl")
return pred
except:
raise Exception("Something went wrong when loading the saved object.")
pred = self.recorder.load_object(self.artifact_path / "port_analysis.pkl")
return pred
def check(self):
artifacts = self.recorder.list_artifacts(self.artifact_path)