Fix the Errors/Warnings when building Qlib's documentation (#1381)

* Fix the Errors/Warnings when building Qlib's documentation

* Fix

* Fix

* Empty

* Test CI

* Add doc compiling checking to CI

* Fix

* Tries to be consistent with Makefile

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

qlib/contrib/evaluate.py

@@ -96,9 +96,11 @@ def indicator_analysis(df, method="mean"):
         index: Index(datetime)
     method : str, optional
         statistics method of pa/ffr, by default "mean"
+
         - if method is 'mean', count the mean statistical value of each trade indicator
         - if method is 'amount_weighted', count the deal_amount weighted mean statistical value of each trade indicator
         - if method is 'value_weighted', count the value weighted mean statistical value of each trade indicator
+
         Note: statistics method of pos is always "mean"
 
     Returns
@@ -154,6 +156,7 @@ def backtest_daily(
         E.g.
 
         .. code-block:: python
+
             # dict
             strategy = {
                 "class": "TopkDropoutStrategy",
@@ -180,7 +183,6 @@ def backtest_daily(
             # 3) specify module path with class name
             #     - "a.b.c.ClassName" getattr(<a.b.c.module>, "ClassName")() will be used.
 
-
     executor : Union[str, dict, BaseExecutor]
         for initializing the outermost executor.
     benchmark: str

qlib/contrib/report/analysis_model/analysis_model_performance.py

@@ -276,8 +276,8 @@ def model_performance_graph(
 ) -> [list, tuple]:
     """Model performance
 
-    :param pred_label: index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[score,
-    label]**. It is usually same as the label of model training(e.g. "Ref($close, -2)/Ref($close, -1) - 1").
+    :param pred_label: index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[score, label]**.
+           It is usually same as the label of model training(e.g. "Ref($close, -2)/Ref($close, -1) - 1").
 
 
             .. code-block:: python

qlib/contrib/report/analysis_position/cumulative_return.py

@@ -218,6 +218,7 @@ def cumulative_return_graph(
 
 
         Graph desc:
+
             - Axis X: Trading day.
             - Axis Y:
             - Above axis Y: `(((Ref($close, -1)/$close - 1) * weight).sum() / weight.sum()).cumsum()`.
@@ -242,7 +243,8 @@ def cumulative_return_graph(
 
 
     :param label_data: `D.features` result; index is `pd.MultiIndex`, index name is [`instrument`, `datetime`]; columns names is [`label`].
-    **The label T is the change from T to T+1**, it is recommended to use ``close``, example: `D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'])`
+
+        **The label T is the change from T to T+1**, it is recommended to use ``close``, example: `D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'])`
 
 
             .. code-block:: python

qlib/contrib/report/analysis_position/rank_label.py

@@ -99,7 +99,8 @@ def rank_label_graph(
 
     :param position: position data; **qlib.backtest.backtest** result.
     :param label_data: **D.features** result; index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[label]**.
-    **The label T is the change from T to T+1**, it is recommended to use ``close``, example: `D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'])`.
+
+        **The label T is the change from T to T+1**, it is recommended to use ``close``, example: `D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'])`.
 
 
             .. code-block:: python

qlib/contrib/strategy/cost_control.py

@@ -25,12 +25,14 @@ class SoftTopkStrategy(WeightStrategyBase):
         common_infra=None,
         **kwargs,
     ):
-        """Parameter
+        """
+        Parameters
+        ----------
         topk : int
             top-N stocks to buy
         risk_degree : float
-            position percentage of total value
-            buy_method :
+            position percentage of total value buy_method:
+
                 rank_fill: assign the weight stocks that rank high first(1/topk max)
                 average_fill: assign the weight to the stocks rank high averagely.
         """
@@ -51,12 +53,19 @@ class SoftTopkStrategy(WeightStrategyBase):
         return self.risk_degree
 
     def generate_target_weight_position(self, score, current, trade_start_time, trade_end_time):
-        """Parameter:
-        score : pred score for this trade date, pd.Series, index is stock_id, contain 'score' column
-        current : current position, use Position() class
-        trade_date : trade date
-        generate target position from score for this date and the current position
-        The cache is not considered in the position
+        """
+        Parameters
+        ----------
+        score:
+            pred score for this trade date, pd.Series, index is stock_id, contain 'score' column
+        current:
+            current position, use Position() class
+        trade_date:
+            trade date
+
+            generate target position from score for this date and the current position
+
+            The cache is not considered in the position
         """
         # TODO:
         # If the current stock list is more than topk(eg. The weights are modified

qlib/contrib/strategy/signal_strategy.py

@@ -103,9 +103,13 @@ class TopkDropoutStrategy(BaseSignalStrategy):
             before sell stock , will check current.get_stock_count(order.stock_id) >= self.hold_thresh.
         only_tradable : bool
             will the strategy only consider the tradable stock when buying and selling.
+
             if only_tradable:
+
                 strategy will make decision with the tradable state of the stock info and avoid buy and sell them.
+
             else:
+
                 strategy will make buy sell decision without checking the tradable state of the stock.
         """
         super().__init__(**kwargs)
@@ -287,9 +291,11 @@ class WeightStrategyBase(BaseSignalStrategy):
             the decision of the strategy will base on the given signal
         trade_exchange : Exchange
             exchange that provides market info, used to deal order and generate report
+
             - If `trade_exchange` is None, self.trade_exchange will be set with common_infra
             - It allowes different trade_exchanges is used in different executions.
             - For example:
+
                 - In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it run faster.
                 - In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
         """
@@ -303,6 +309,7 @@ class WeightStrategyBase(BaseSignalStrategy):
     def generate_target_weight_position(self, score, current, trade_start_time, trade_end_time):
         """
         Generate target position from score for this date and the current position.The cash is not considered in the position
+
         Parameters
         -----------
         score : pd.Series
@@ -355,12 +362,14 @@ class EnhancedIndexingStrategy(WeightStrategyBase):
 
     Users need to prepare their risk model data like below:
 
-    ├── /path/to/riskmodel
-    ├──── 20210101
-    ├────── factor_exp.{csv|pkl|h5}
-    ├────── factor_cov.{csv|pkl|h5}
-    ├────── specific_risk.{csv|pkl|h5}
-    ├────── blacklist.{csv|pkl|h5}  # optional
+    .. code-block:: text
+
+        ├── /path/to/riskmodel
+        ├──── 20210101
+        ├────── factor_exp.{csv|pkl|h5}
+        ├────── factor_cov.{csv|pkl|h5}
+        ├────── specific_risk.{csv|pkl|h5}
+        ├────── blacklist.{csv|pkl|h5}  # optional
 
     The risk model data can be obtained from risk data provider. You can also use
     `qlib.model.riskmodel.structured.StructuredCovEstimator` to prepare these data.