exposing parameters for metatrand too

IncrementalTrader/strategies/metatrend.py

@@ -65,6 +65,11 @@ class MetaTrendStrategy(IncStrategyBase):
             name: Strategy name/identifier
             weight: Strategy weight for combination (default: 1.0)
             params: Strategy parameters
+                - timeframe: Primary timeframe for analysis (default: "15min")
+                - enable_logging: Enable detailed logging (default: False)
+                - supertrend_periods: List of periods for Supertrend indicators (default: [12, 10, 11])
+                - supertrend_multipliers: List of multipliers for Supertrend indicators (default: [3.0, 1.0, 2.0])
+                - min_trend_agreement: Minimum fraction of indicators that must agree (default: 1.0, meaning all)
         """
         super().__init__(name, weight, params)
         
@@ -76,12 +81,28 @@ class MetaTrendStrategy(IncStrategyBase):
         if self.enable_logging:
             logger.setLevel(logging.DEBUG)
         
-        # Initialize Supertrend collection with exact parameters from original strategy
-        self.supertrend_configs = [
-            (12, 3.0),  # period=12, multiplier=3.0
-            (10, 1.0),  # period=10, multiplier=1.0
-            (11, 2.0)   # period=11, multiplier=2.0
-        ]
+        # Get configurable Supertrend parameters from params or use defaults
+        default_periods = [12, 10, 11]
+        default_multipliers = [3.0, 1.0, 2.0]
+        
+        supertrend_periods = self.params.get("supertrend_periods", default_periods)
+        supertrend_multipliers = self.params.get("supertrend_multipliers", default_multipliers)
+        
+        # Validate parameters
+        if len(supertrend_periods) != len(supertrend_multipliers):
+            raise ValueError(f"supertrend_periods ({len(supertrend_periods)}) and "
+                           f"supertrend_multipliers ({len(supertrend_multipliers)}) must have same length")
+        
+        if len(supertrend_periods) < 1:
+            raise ValueError("At least one Supertrend indicator is required")
+        
+        # Initialize Supertrend collection with configurable parameters
+        self.supertrend_configs = list(zip(supertrend_periods, supertrend_multipliers))
+        
+        # Store agreement threshold
+        self.min_trend_agreement = self.params.get("min_trend_agreement", 1.0)
+        if not 0.0 <= self.min_trend_agreement <= 1.0:
+            raise ValueError("min_trend_agreement must be between 0.0 and 1.0")
         
         self.supertrend_collection = SupertrendCollection(self.supertrend_configs)
         
@@ -101,6 +122,8 @@ class MetaTrendStrategy(IncStrategyBase):
         
         logger.info(f"MetaTrendStrategy initialized: timeframe={self.primary_timeframe}, "
                    f"aggregation_enabled={self._timeframe_aggregator is not None}")
+        logger.info(f"Supertrend configs: {self.supertrend_configs}, "
+                   f"min_agreement={self.min_trend_agreement}")
         
         if self.enable_logging:
             logger.info(f"Using new timeframe utilities with mathematically correct aggregation")
@@ -292,10 +315,10 @@ class MetaTrendStrategy(IncStrategyBase):
         """
         Calculate meta-trend from SupertrendCollection results.
         
-        Meta-trend logic (matching original DefaultStrategy):
-        - All 3 Supertrends must agree for directional signal
-        - If all trends are the same, meta-trend = that trend
-        - If trends disagree, meta-trend = 0 (neutral)
+        Meta-trend logic (enhanced with configurable agreement threshold):
+        - Uses min_trend_agreement to determine consensus requirement
+        - If agreement threshold is met for a direction, meta-trend = that direction
+        - If no consensus, meta-trend = 0 (neutral)
         
         Args:
             supertrend_results: Results from SupertrendCollection.update()
@@ -304,12 +327,26 @@ class MetaTrendStrategy(IncStrategyBase):
             int: Meta-trend value (1, -1, or 0)
         """
         trends = supertrend_results['trends']
+        total_indicators = len(trends)
         
-        # Check if all trends agree
-        if all(trend == trends[0] for trend in trends):
-            return trends[0]  # All agree: return the common trend
+        if total_indicators == 0:
+            return 0
+        
+        # Count votes for each direction
+        uptrend_votes = sum(1 for trend in trends if trend == 1)
+        downtrend_votes = sum(1 for trend in trends if trend == -1)
+        
+        # Calculate agreement percentages
+        uptrend_agreement = uptrend_votes / total_indicators
+        downtrend_agreement = downtrend_votes / total_indicators
+        
+        # Check if agreement threshold is met
+        if uptrend_agreement >= self.min_trend_agreement:
+            return 1
+        elif downtrend_agreement >= self.min_trend_agreement:
+            return -1
         else:
-            return 0  # Neutral when trends disagree
+            return 0  # No consensus
     
     def _check_entry_condition(self) -> bool:
         """