Fix backtest

docs/component/strategy.rst

@@ -84,79 +84,136 @@ Usage & Example
 ====================
 ``Portfolio Strategy`` can be specified in the ``Intraday Trading(Backtest)``, the example is as follows.
 
-.. code-block:: python
-
-    from pprint import pprint
-
-    import qlib
-    import pandas as pd
-    from qlib.utils.time import Freq
-    from qlib.utils import flatten_dict
-    from qlib.backtest import backtest, executor
-    from qlib.contrib.evaluate import risk_analysis
-    from qlib.contrib.strategy import TopkDropoutStrategy
-
-    # init qlib
-    qlib.init(provider_uri=<qlib data dir>)
-
-    CSI300_BENCH = "SH000300"
-    FREQ = "day"
-    STRATEGY_CONFIG = {
-        "topk": 50,
-        "n_drop": 5,
-        # pred_score, pd.Series
-        "signal": pred_score,
-    }
-
-    EXECUTOR_CONFIG = {
-        "time_per_step": "day",
-        "generate_portfolio_metrics": True,
-    }
-
-    backtest_config = {
-        "start_time": "2017-01-01",
-        "end_time": "2020-08-01",
-        "account": 100000000,
-        "benchmark": CSI300_BENCH,
-        "exchange_kwargs": {
-            "freq": FREQ,
-            "limit_threshold": 0.095,
-            "deal_price": "close",
-            "open_cost": 0.0005,
-            "close_cost": 0.0015,
-            "min_cost": 5,
-        },
-    }
-
-    # strategy object
-    strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
-    # executor object
-    executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
-    # backtest
-    portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
-    analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
-    # backtest info
-    report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)
-
-    # analysis
+- daily
+
+    .. code-block:: python
+
+        from pprint import pprint
+
+        import qlib
+        import pandas as pd
+        from qlib.utils.time import Freq
+        from qlib.utils import flatten_dict
+        from qlib.contrib.evaluate import backtest_daily
+        from qlib.contrib.evaluate import risk_analysis
+        from qlib.contrib.strategy import TopkDropoutStrategy
+
+        # init qlib
+        qlib.init(provider_uri=<qlib data dir>)
+
+        CSI300_BENCH = "SH000300"
+        FREQ = "day"
+        STRATEGY_CONFIG = {
+            "topk": 50,
+            "n_drop": 5,
+            # pred_score, pd.Series
+            "signal": pred_score,
+        }
+
+
+        strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
+        portfolio_metric_dict, indicator_dict = backtest_daily(
+            start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_obj, executor=executor_obj
+        )
+        analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
+
+        report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)
+        analysis = dict()
+        analysis["excess_return_without_cost"] = risk_analysis(
+            report_normal["return"] - report_normal["bench"], freq=analysis_freq
+        )
+        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
+        # log metrics
+        analysis_dict = flatten_dict(analysis_df["risk"].unstack().T.to_dict())
+        # print out results
+        pprint(f"The following are analysis results of benchmark return({analysis_freq}).")
+        pprint(risk_analysis(report_normal["bench"], freq=analysis_freq))
+        pprint(f"The following are analysis results of the excess return without cost({analysis_freq}).")
+        pprint(analysis["excess_return_without_cost"])
+        pprint(f"The following are analysis results of the excess return with cost({analysis_freq}).")
+        pprint(analysis["excess_return_with_cost"])
+
+
+
+- nested decision execution
+
+    .. code-block:: python
+
+        from pprint import pprint
+
+        import qlib
+        import pandas as pd
+        from qlib.utils.time import Freq
+        from qlib.utils import flatten_dict
+        from qlib.backtest import backtest, executor
+        from qlib.contrib.evaluate import risk_analysis
+        from qlib.contrib.strategy import TopkDropoutStrategy
+
+        # init qlib
+        qlib.init(provider_uri=<qlib data dir>)
+
+        CSI300_BENCH = "SH000300"
+        FREQ = "day"
+        STRATEGY_CONFIG = {
+            "topk": 50,
+            "n_drop": 5,
+            # pred_score, pd.Series
+            "signal": pred_score,
+        }
+
+        EXECUTOR_CONFIG = {
+            "time_per_step": "day",
+            "generate_portfolio_metrics": True,
+        }
+
+        backtest_config = {
+            "start_time": "2017-01-01",
+            "end_time": "2020-08-01",
+            "account": 100000000,
+            "benchmark": CSI300_BENCH,
+            "exchange_kwargs": {
+                "freq": FREQ,
+                "limit_threshold": 0.095,
+                "deal_price": "close",
+                "open_cost": 0.0005,
+                "close_cost": 0.0015,
+                "min_cost": 5,
+            },
+        }
+
+        # strategy object
+        strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
+        # executor object
+        executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
+        # backtest
+        portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
+        analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
+        # backtest info
+        report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)
+
+        # analysis
         analysis = dict()
-    analysis["excess_return_without_cost"] = risk_analysis(
-        report_normal["return"] - report_normal["bench"], freq=analysis_freq
-    )
-    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
-    # log metrics
-    analysis_dict = flatten_dict(analysis_df["risk"].unstack().T.to_dict())
-    # print out results
-    pprint(f"The following are analysis results of benchmark return({analysis_freq}).")
-    pprint(risk_analysis(report_normal["bench"], freq=analysis_freq))
-    pprint(f"The following are analysis results of the excess return without cost({analysis_freq}).")
-    pprint(analysis["excess_return_without_cost"])
-    pprint(f"The following are analysis results of the excess return with cost({analysis_freq}).")
-    pprint(analysis["excess_return_with_cost"])
+        analysis["excess_return_without_cost"] = risk_analysis(
+            report_normal["return"] - report_normal["bench"], freq=analysis_freq
+        )
+        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
+        # log metrics
+        analysis_dict = flatten_dict(analysis_df["risk"].unstack().T.to_dict())
+        # print out results
+        pprint(f"The following are analysis results of benchmark return({analysis_freq}).")
+        pprint(risk_analysis(report_normal["bench"], freq=analysis_freq))
+        pprint(f"The following are analysis results of the excess return without cost({analysis_freq}).")
+        pprint(analysis["excess_return_without_cost"])
+        pprint(f"The following are analysis results of the excess return with cost({analysis_freq}).")
+        pprint(analysis["excess_return_with_cost"])
 
 
 To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.

qlib/backtest/__init__.py

@@ -186,12 +186,10 @@ def get_strategy_executor(
     trade_exchange = get_exchange(**exchange_kwargs)
 
     common_infra = CommonInfrastructure(trade_account=trade_account, trade_exchange=trade_exchange)
-    trade_strategy = init_instance_by_config(strategy, accept_types=BaseStrategy, common_infra=common_infra)
-    if isinstance(strategy, BaseStrategy):
-        trade_strategy.reset_common_infra(common_infra)
-    trade_executor = init_instance_by_config(executor, accept_types=BaseExecutor, common_infra=common_infra)
-    if isinstance(executor, BaseExecutor):
-        trade_executor.reset_common_infra(common_infra)
+    trade_strategy = init_instance_by_config(strategy, accept_types=BaseStrategy)
+    trade_strategy.reset_common_infra(common_infra)
+    trade_executor = init_instance_by_config(executor, accept_types=BaseExecutor)
+    trade_executor.reset_common_infra(common_infra)
 
     return trade_strategy, trade_executor
 

qlib/backtest/exchange.py

@@ -231,7 +231,7 @@ def get_quote_from_qlib(self):
                 self.extra_quote["limit_buy"] = False
                 self.logger.warning("No limit_buy set for extra_quote. All stock will be able to be bought.")
             assert set(self.extra_quote.columns) == set(self.quote_df.columns) - {"$change"}
-            self.quote_df = pd.concat([self.quote_df, extra_quote], sort=False, axis=0)
+            self.quote_df = pd.concat([self.quote_df, self.extra_quote], sort=False, axis=0)
 
     LT_TP_EXP = "(exp)"  # Tuple[str, str]
     LT_FLT = "float"  # float
@@ -736,8 +736,8 @@ def _calc_trade_info_by_order(self, order, position: Position, dealt_order_amoun
 
         # TODO: the adjusted cost ratio can be overestimated as deal_amount will be clipped in the next steps
         trade_val = order.deal_amount * trade_price
-        assert trade_val > 1e-5, f"trade_val <= 1e-5, order info: {order}"
         if not total_trade_val or np.isnan(total_trade_val):
+            # TODO: assert trade_val == 0, f"trade_val != 0, total_trade_val: {total_trade_val}; order info: {order}"
             adj_cost_ratio = self.impact_cost
         else:
             adj_cost_ratio = self.impact_cost * (trade_val / total_trade_val) ** 2