Fix backtest (#719)

* modify FileStorage to support multiple freqs

* modify backtest's sample documentation

* change the logging level of read data exception from error to debug

* fix the backtest exception when volume is 0 or np.nan

* fix test_storage.py

* add backtest_daily

* modify backtest_daily's docstring

* add __repr__/__str__ to Position

* fix the bug of nested_decision_execution example

Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

docs/component/strategy.rst

@@ -84,31 +84,125 @@ Usage & Example
 ====================
 ``Portfolio Strategy`` can be specified in the ``Intraday Trading(Backtest)``, the example is as follows.
 
-.. code-block:: python
+- daily
 
-    from qlib.contrib.strategy.strategy import TopkDropoutStrategy
-    from qlib.contrib.evaluate import backtest
-    STRATEGY_CONFIG = {
-        "topk": 50,
-        "n_drop": 5,
-    }
-    BACKTEST_CONFIG = {
-        "limit_threshold": 0.095,
-        "account": 100000000,
-        "benchmark": BENCHMARK,
-        "deal_price": "close",
-        "open_cost": 0.0005,
-        "close_cost": 0.0015,
-        "min_cost": 5,
-        
-    }
-    # use default strategy
-    strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
+    .. 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"
+        STRATEGY_CONFIG = {
+            "topk": 50,
+            "n_drop": 5,
+            # pred_score, pd.Series
+            "signal": pred_score,
+        }
+
+
+        strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
+        report_normal, positions_normal = backtest_daily(
+            start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_obj
+        )
+        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
+        pprint(analysis_df)
+
+
+
+- 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"])
 
-    # pred_score is the `prediction score` output by Model
-    report_normal, positions_normal = backtest(
-        pred_score, strategy=strategy, **BACKTEST_CONFIG
-    )
 
 To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.