update port_ana_record

microsoft · bxdd · Mar 19, 2021 · Apr 21, 2021 · Apr 21, 2021 · Apr 22, 2021
commit 49cdaf8f5da528daeb2b0a967073c275388e95ad
diff --git a/qlib/contrib/backtest/account.py b/qlib/contrib/backtest/account.py
@@ -214,6 +214,7 @@ def update_report(self, trade_start_time, trade_end_time, trade_exchange):
 
         # finish today's updation
         # reset the daily variables
+        self.rtn = 0
         self.ct = 0
         self.to = 0
 

diff --git a/qlib/contrib/backtest/backtest.py b/qlib/contrib/backtest/backtest.py
@@ -9,7 +9,7 @@
 
 def backtest(start_time, end_time, trade_strategy, trade_env, benchmark, account):
 
-    trade_account = Account(init_cash=account)
+    trade_account = Account(init_cash=account, benchmark=benchmark, start_time=start_time, end_time=end_time)
     trade_env.reset(start_time=start_time, end_time=end_time, trade_account=trade_account)
     trade_strategy.reset(start_time=start_time, end_time=end_time)
 

diff --git a/qlib/workflow/record_temp.py b/qlib/workflow/record_temp.py
@@ -2,6 +2,7 @@
 #  Licensed under the MIT License.
 
 import re
+import warnings
 import pandas as pd
 from pathlib import Path
 from pprint import pprint
@@ -223,18 +224,23 @@ class PortAnaRecord(SignalRecord):
 
     artifact_path = "portfolio_analysis"
 
-    def __init__(self, recorder, config, **kwargs):
+    def __init__(self, recorder, config, risk_analysis_dep, **kwargs):
         """
         config["strategy"] : dict
             define the strategy class as well as the kwargs.
+        config["env"] : dict
+            define the env class as well as the kwargs.
         config["backtest"] : dict
             define the backtest kwargs.
+        risk_analysis_dep : int
+            risk analyze the dep'th env report
         """
         super().__init__(recorder=recorder, **kwargs)
 
         self.strategy_config = config["strategy"]
         self.env_config = config["env"]
         self.backtest_config = config["backtest"]
+        self.risk_analysis_dep = risk_analysis_dep
 
     def generate(self, **kwargs):
         # check previously stored prediction results
@@ -245,31 +251,34 @@ def generate(self, **kwargs):
 
         # custom strategy and get backtest
         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):
+        for report_dep, (report_normal, positions_normal) in enumerate(report_list):
             if report_dict is None:
+                if self.risk_analysis_dep == report_dep:
+                    warnings.warn(f"the report in dep {risk_analysis_dep} is None, please set the corresponding env with `generate_report==True`")
                 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())
+
+            self.recorder.save_objects(**{f"report_normal_{report_dep}.pkl": report_normal}, artifact_path=PortAnaRecord.get_path())
+            self.recorder.save_objects(**{f"positions_norma_{report_dep}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"])
+            self.risk_analysis_dep == report_dep:
+                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_dep}": analysis_df}, artifact_path=PortAnaRecord.get_path())
+                logger.info(
+                    f"Portfolio analysis record 'port_analysis_{report_dep}.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 [