Add backtest example to online simulation (#984)

docs/component/strategy.rst

@@ -161,12 +161,9 @@ Running backtest
             start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_obj
         )
         analysis = dict()
-        analysis["excess_return_without_cost"] = risk_analysis(
+        # default frequency will be daily (i.e. "day")
-            report_normal["return"] - report_normal["bench"], freq=analysis_freq
+        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["excess_return_with_cost"] = risk_analysis(
-            report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
-        )
 
         analysis_df = pd.concat(analysis)  # type: pd.DataFrame
         pprint(analysis_df)

examples/online_srv/online_management_simulate.py

@@ -15,6 +15,10 @@ from qlib.workflow.online.strategy import RollingStrategy
 from qlib.workflow.task.gen import RollingGen
 from qlib.workflow.task.manage import TaskManager
 from qlib.tests.config import CSI100_RECORD_LGB_TASK_CONFIG_ONLINE, CSI100_RECORD_XGBOOST_TASK_CONFIG_ONLINE
+import pandas as pd
+from qlib.contrib.evaluate import backtest_daily
+from qlib.contrib.evaluate import risk_analysis
+from qlib.contrib.strategy import TopkDropoutStrategy
 
 
 class OnlineSimulationExample:
@@ -30,6 +34,7 @@ class OnlineSimulationExample:
         start_time="2018-09-10",
         end_time="2018-10-31",
         tasks=None,
+        trainer="TrainerR",
     ):
         """
         Init OnlineManagerExample.
@@ -60,7 +65,13 @@ class OnlineSimulationExample:
         self.rolling_gen = RollingGen(
             step=rolling_step, rtype=RollingGen.ROLL_SD, ds_extra_mod_func=None
         )  # The rolling tasks generator, ds_extra_mod_func is None because we just need to simulate to 2018-10-31 and needn't change the handler end time.
-        self.trainer = TrainerRM(self.exp_name, self.task_pool)  # Also can be TrainerR, TrainerRM, DelayTrainerR
+        if trainer == "TrainerRM":
+            self.trainer = TrainerRM(self.exp_name, self.task_pool)
+        elif trainer == "TrainerR":
+            self.trainer = TrainerR(self.exp_name)
+        else:
+            # TODO: support all the trainers: TrainerR, TrainerRM, DelayTrainerR
+            raise NotImplementedError(f"This type of input is not supported")
         self.rolling_online_manager = OnlineManager(
             RollingStrategy(exp_name, task_template=tasks, rolling_gen=self.rolling_gen),
             trainer=self.trainer,
@@ -70,7 +81,8 @@ class OnlineSimulationExample:
 
     # Reset all things to the first status, be careful to save important data
     def reset(self):
-        TaskManager(self.task_pool).remove()
+        if isinstance(self.trainer, TrainerRM):
+            TaskManager(self.task_pool).remove()
         exp = R.get_exp(experiment_name=self.exp_name)
         for rid in exp.list_recorders():
             exp.delete_recorder(rid)
@@ -84,7 +96,30 @@ class OnlineSimulationExample:
         print("========== collect results ==========")
         print(self.rolling_online_manager.get_collector()())
         print("========== signals ==========")
-        print(self.rolling_online_manager.get_signals())
+        signals = self.rolling_online_manager.get_signals()
+        print(signals)
+        # Backtesting
+        # - the code is based on this example https://qlib.readthedocs.io/en/latest/component/strategy.html
+        CSI300_BENCH = "SH000903"
+        STRATEGY_CONFIG = {
+            "topk": 30,
+            "n_drop": 3,
+            "signal": signals.to_frame("score"),
+        }
+        strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
+        report_normal, positions_normal = backtest_daily(
+            start_time=signals.index.get_level_values("datetime").min(),
+            end_time=signals.index.get_level_values("datetime").max(),
+            strategy=strategy_obj,
+        )
+        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
+        pprint(analysis_df)
 
     def worker(self):
         # train tasks by other progress or machines for multiprocessing

qlib/contrib/model/gbdt.py

@@ -71,6 +71,7 @@ class LGBModel(ModelFT, LightGBMFInt):
         early_stopping_callback = lgb.early_stopping(
             self.early_stopping_rounds if early_stopping_rounds is None else early_stopping_rounds
         )
+        # NOTE: if you encounter error here. Please upgrade your lightgbm
         verbose_eval_callback = lgb.log_evaluation(period=verbose_eval)
         evals_result_callback = lgb.record_evaluation(evals_result)
         self.model = lgb.train(