update workflow_by_code & update executor

examples/workflow_by_code.py

@@ -66,32 +66,45 @@ if __name__ == "__main__":
         },
     }
 
+    # model initialization
+    model = init_instance_by_config(task["model"])
+    dataset = init_instance_by_config(task["dataset"])
+
     port_analysis_config = {
+        "executor": {
+            "class": "SimulatorExecutor",
+            "module_path": "qlib.backtest.executor",
+            "kwargs": {
+                "time_per_step": "day",
+                "generate_report": True,
+            },
+        },
         "strategy": {
             "class": "TopkDropoutStrategy",
-            "module_path": "qlib.contrib.strategy.strategy",
+            "module_path": "qlib.contrib.strategy.model_strategy",
             "kwargs": {
+                "model": model,
+                "dataset": dataset,
                 "topk": 50,
                 "n_drop": 5,
             },
         },
         "backtest": {
-            "verbose": False,
-            "limit_threshold": 0.095,
+            "start_time": "2017-01-01",
+            "end_time": "2020-08-01",
             "account": 100000000,
             "benchmark": benchmark,
-            "deal_price": "close",
-            "open_cost": 0.0005,
-            "close_cost": 0.0015,
-            "min_cost": 5,
-            "return_order": True,
+            "exchange_kwargs": {
+                "freq": "day",
+                "limit_threshold": 0.095,
+                "deal_price": "close",
+                "open_cost": 0.0005,
+                "close_cost": 0.0015,
+                "min_cost": 5,
+            },
         },
     }
 
-    # model initialization
-    model = init_instance_by_config(task["model"])
-    dataset = init_instance_by_config(task["dataset"])
-
     # NOTE: This line is optional
     # It demonstrates that the dataset can be used standalone.
     example_df = dataset.prepare("train")
@@ -110,5 +123,5 @@ if __name__ == "__main__":
 
         # backtest. If users want to use backtest based on their own prediction,
         # please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.
-        par = PortAnaRecord(recorder, port_analysis_config)
+        par = PortAnaRecord(recorder, port_analysis_config, "day")
         par.generate()

qlib/backtest/__init__.py

@@ -8,6 +8,7 @@ from .backtest import backtest as backtest_func
 from .backtest import collect_data as data_generator
 
 from .utils import CommonInfrastructure
+from .order import Order
 from ..strategy.base import BaseStrategy
 from ..utils import init_instance_by_config
 from ..log import get_module_logger

qlib/backtest/executor.py

@@ -118,6 +118,9 @@ class BaseExecutor:
     def get_report(self):
         raise NotImplementedError("get_report is not implemented!")
 
+    def get_all_executor(self):
+        return [self]
+
 
 class NestedExecutor(BaseExecutor):
     """
@@ -244,6 +247,9 @@ class NestedExecutor(BaseExecutor):
             sub_env_report_dict.update({f"{_count}{_freq}": (_report, _positions)})
         return sub_env_report_dict
 
+    def get_all_executor(self):
+        return [self, *self.inner_executor.get_all_executor()]
+
 
 class SimulatorExecutor(BaseExecutor):
     """Executor that simulate the true market"""

qlib/backtest/order.py

@@ -11,10 +11,11 @@ class Order:
     stock_id : str
     amount : float
     start_time : pd.Timestamp
-        closed start time for order generation
+        closed start time for order trading
     end_time : pd.Timestamp
-        closed end time for order generation
-    direction : Order.SELL for sell; Order.BUY for buy
+        closed end time for order trading
+    direction : int
+        Order.SELL for sell; Order.BUY for buy
     factor : float
             presents the weight factor assigned in Exchange()
     """