add default executor config & update bug in indicator

qlib/contrib/evaluate.py

@@ -84,29 +84,33 @@ def indicator_analysis(df, method="mean"):
 
         index: Index(datetime)
     method : str, optional
-        statistics method, by default "mean"
+        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 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
     -------
     pd.DataFrame
-        statistical value of each trade indicator
+        statistical value of each trade indicators
     """
-    indicators_df = df[["pa", "pos", "ffr"]]
-
-    if method == "mean":
-        res = indicators_df.mean()
-    elif method == "amount_weighted":
-        weights = df["amount"].abs()
-        res = indicators_df.mul(weights, axis=0).sum() / weights.sum()
-    elif method == "value_weighted":
-        weights = df["value"].abs()
-        res = indicators_df.mul(weights, axis=0).sum() / weights.sum()
-    else:
+    weights_dict = {
+        "mean": df["count"],
+        "amount_weighted": df["amount"].abs(),
+        "value_weighted": df["value"].abs(),
+    }
+    if method not in weights_dict:
         raise ValueError(f"indicator_analysis method {method} is not supported!")
 
+    # statistic pa/ffr indicator
+    indicators_df = df[["ffr", "pa"]]
+    weights = weights_dict.get(method)
+    res = indicators_df.mul(weights, axis=0).sum() / weights.sum()
+
+    # statistic pos
+    weights = weights_dict.get("mean")
+    res.loc["pos"] = df["pos"].mul(weights).sum() / weights.sum()
     res = res.to_frame("value")
     return res
 

qlib/contrib/strategy/rule_strategy.py

@@ -414,12 +414,12 @@ class SBBStrategyEMA(SBBStrategyBase):
             # if EMA signal > 0, return long trend
             elif _sample_signal.iloc[0] > 0:
                 return self.TREND_LONG
-            # if EMA signal > 0, return short trend
+            # if EMA signal < 0, return short trend
             else:
                 return self.TREND_SHORT
 
 
-class VAStrategy(BaseStrategy):
+class ACStrategy(BaseStrategy):
     def __init__(
         self,
         lamb: float = 1e-6,
@@ -451,7 +451,7 @@ class VAStrategy(BaseStrategy):
         if isinstance(instruments, str):
             self.instruments = D.instruments(instruments)
         self.freq = freq
-        super(VAStrategy, self).__init__(outer_trade_decision, level_infra, common_infra, **kwargs)
+        super(ACStrategy, self).__init__(outer_trade_decision, level_infra, common_infra, **kwargs)
 
         if trade_exchange is not None:
             self.trade_exchange = trade_exchange
@@ -483,7 +483,7 @@ class VAStrategy(BaseStrategy):
             - It should include `trade_account`, used to get position
             - It should include `trade_exchange`, used to provide market info
         """
-        super(VAStrategy, self).reset_common_infra(common_infra)
+        super(ACStrategy, self).reset_common_infra(common_infra)
 
         if common_infra.has("trade_exchange"):
             self.trade_exchange = common_infra.get("trade_exchange")
@@ -508,7 +508,7 @@ class VAStrategy(BaseStrategy):
         ----------
         outer_trade_decision : List[Order], optional
         """
-        super(VAStrategy, self).reset(outer_trade_decision=outer_trade_decision, **kwargs)
+        super(ACStrategy, self).reset(outer_trade_decision=outer_trade_decision, **kwargs)
         if outer_trade_decision is not None:
             self.trade_amount = {}
             # init the trade amount of order and  predicted trade trend