fix trade time bug

examples/highfreq/backtest/workflow.py

@@ -10,7 +10,7 @@ from qlib.config import REG_CN
 
 from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
 from qlib.workflow import R
-from qlib.workflow.record_temp import PortAnaRecord
+from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
 from qlib.tests.data import GetData
 
 if __name__ == "__main__":
@@ -64,9 +64,9 @@ if __name__ == "__main__":
                     "kwargs": data_handler_config,
                 },
                 "segments": {
-                    "train": ("2012-01-01", "2014-12-31"),
+                    "train": ("2008-01-01", "2014-12-31"),
                     "valid": ("2015-01-01", "2016-12-31"),
-                    "test": ("2017-01-01", "2018-01-31"),
+                    "test": ("2017-01-01", "2020-08-01"),
                 },
             },
         },
@@ -74,17 +74,16 @@ if __name__ == "__main__":
     # model initialization
     model = init_instance_by_config(task["model"])
     dataset = init_instance_by_config(task["dataset"])
-    model.fit(dataset)
 
-    trade_start_time = "2017-01-31"
-    trade_end_time = "2018-01-31"
+    trade_start_time = "2017-01-01"
+    trade_end_time = "2020-08-01"
 
     port_analysis_config = {
         "strategy": {
             "class": "TopkDropoutStrategy",
             "module_path": "qlib.contrib.strategy.model_strategy",
             "kwargs": {
-                "step_bar": "week",
+                "step_bar": "day",
                 "model": model,
                 "dataset": dataset,
                 "topk": 50,
@@ -92,28 +91,12 @@ if __name__ == "__main__":
             },
         },
         "env": {
-            "class": "SplitEnv",
+            "class": "SimulatorEnv",
             "module_path": "qlib.contrib.backtest.env",
             "kwargs": {
-                "step_bar": "week",
-                "sub_env": {
-                    "class": "SimulatorEnv",
-                    "module_path": "qlib.contrib.backtest.env",
-                    "kwargs": {
-                        "step_bar": "day",
-                        "verbose": True,
-                        "generate_report": True,
-                    },
-                },
-                "sub_strategy": {
-                    "class": "SBBStrategyEMA",
-                    "module_path": "qlib.contrib.strategy.rule_strategy",
-                    "kwargs": {
-                        "step_bar": "day",
-                        "freq": "day",
-                        "instruments": "csi300",
-                    },
-                },
+                "step_bar": "day",
+                "verbose": True,
+                "generate_report": True,
             },
         },
         "backtest": {
@@ -129,9 +112,18 @@ if __name__ == "__main__":
             "min_cost": 5,
         },
     }
+        
     with R.start(experiment_name="highfreq_backtest"):
+        R.log_params(**flatten_dict(task))
+        model.fit(dataset)
+        R.save_objects(**{"params.pkl": model})
+
+        # prediction
+        recorder = R.get_recorder()
+        sr = SignalRecord(model, dataset, recorder)
+        sr.generate()
+
         # 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.
-        recorder = R.get_recorder()
         par = PortAnaRecord(recorder, port_analysis_config, "day")
-        par.generate()
+        par.generate()

qlib/contrib/backtest/account.py

@@ -94,7 +94,7 @@ class Account:
 
     def _sample_benchmark(self, bench, trade_start_time, trade_end_time):
         def cal_change(x):
-            return x.prod() - 1
+            return (x + 1).prod() - 1
 
         _ret = sample_feature(bench, trade_start_time, trade_end_time, method=cal_change)
         return 0 if _ret is None else _ret

qlib/contrib/backtest/env.py

@@ -49,7 +49,7 @@ class BaseTradeCalendar:
     def _get_calendar_time(self, trade_index=1, shift=0):
         trade_index = trade_index - shift
         calendar_index = self.start_index + trade_index
-        return self.calendar[calendar_index - 1], self.calendar[calendar_index]
+        return self.calendar[calendar_index - 1], self.calendar[calendar_index] - pd.Timedelta(seconds=1)
 
     def finished(self):
         return self.trade_index >= self.trade_len - 1

qlib/contrib/strategy/model_strategy.py

@@ -51,7 +51,7 @@ class TopkDropoutStrategy(ModelStrategy):
                 strategy will make decision with the tradable state of the stock info and avoid buy and sell them.
         """
         super(TopkDropoutStrategy, self).__init__(
-            step_bar, model, dataset, start_time, end_time, trade_exchange=trade_exchange
+            step_bar, model, dataset, start_time, end_time, trade_exchange=trade_exchange, **kwargs
         )
         self.topk = topk
         self.n_drop = n_drop

qlib/contrib/strategy/rule_strategy.py

@@ -11,16 +11,30 @@ from ..backtest.order import Order
 
 
 class TWAPStrategy(RuleStrategy, TradingEnhancement):
-    def reset(self, trade_order_list=None, **kwargs):
+    def __init__(
+        self,
+        step_bar,
+        start_time=None,
+        end_time=None,
+        trade_exchange=None,
+        **kwargs,
+    ):
+        super(TWAPStrategy, self).__init__(
+            step_bar, start_time, end_time, trade_exchange=trade_exchange, **kwargs
+        )
+
+    def reset(self, trade_order_list=None, trade_exchange=None, **kwargs):
         super(TWAPStrategy, self).reset(**kwargs)
         TradingEnhancement.reset(self, trade_order_list=trade_order_list)
         if trade_order_list:
             self.trade_amount = {}
             for order in self.trade_order_list:
                 self.trade_amount[(order.stock_id, order.direction)] = order.amount // self.trade_len
+        if trade_exchange:
+            self.trade_exchange = trade_exchange
 
     def generate_order_list(self, **kwargs):
-        super(TopkDropoutStrategy, self).step()
+        super(TWAPStrategy, self).step()
         trade_start_time, trade_end_time = self._get_calendar_time(self.trade_index)
         order_list = []
         for order in self.trade_order_list:
@@ -44,8 +58,19 @@ class SBBStrategyBase(RuleStrategy, TradingEnhancement):
     TREND_MID = 0
     TREND_SHORT = 1
     TREND_LONG = 2
+    def __init__(
+        self,
+        step_bar,
+        start_time=None,
+        end_time=None,
+        trade_exchange=None,
+        **kwargs,
+    ):
+        super(SBBStrategyBase, self).__init__(
+            step_bar, start_time, end_time, trade_exchange=trade_exchange, **kwargs
+        )
 
-    def reset(self, trade_order_list=None, **kwargs):
+    def reset(self, trade_order_list=None, trade_exchange=None, **kwargs):
         super(SBBStrategyBase, self).reset(**kwargs)
         TradingEnhancement.reset(self, trade_order_list=trade_order_list)
         if trade_order_list:
@@ -54,6 +79,8 @@ class SBBStrategyBase(RuleStrategy, TradingEnhancement):
             for order in self.trade_order_list:
                 self.trade_amount[(order.stock_id, order.direction)] = order.amount // self.trade_len
                 self.trade_trend[(order.stock_id, order.direction)] = self.TREND_MID
+        if trade_exchange:
+            self.trade_exchange = trade_exchange
 
     def _pred_price_trend(self, stock_id, pred_start_time=None, pred_end_time=None):
         raise NotImplementedError("pred_price_trend method is not implemented!")
@@ -127,11 +154,12 @@ class SBBStrategyEMA(SBBStrategyBase):
         step_bar,
         start_time=None,
         end_time=None,
+        trade_exchange=None,
         instruments="csi300",
         freq="day",
         **kwargs,
     ):
-        super(SBBStrategyEMA, self).__init__(step_bar, start_time, end_time, **kwargs)
+        super(SBBStrategyEMA, self).__init__(step_bar, start_time, end_time, trade_exchange=trade_exchange, **kwargs)
         if instruments is None:
             warnings.warn("`instruments` is not set, will load all stocks")
             self.instruments = "all"