排查没有n笔交易+1

config_examples/staticgrid.json

@@ -39,6 +39,8 @@
     "entry": 5,
     "exit": 15
   },
+  "cooldown_volume": 0,
+  "action_space": "default",
   "entry_pricing": {
     "price_side": "other",
     "use_order_book": true,

freqtrade/templates/staticgrid.py

@@ -15,6 +15,9 @@ class StaticGrid(IStrategy):
     use_exit_signal = True
     position_adjustment_enable = True
     max_entry_position_adjustment = -1
+    
+    # Lock configuration - disable lockout to allow immediate re-entry
+    cooldown_candles = 0  # No cooldown between trades
 
     # Grid parameters
     LOWER = 1500.0   # Minimum price
@@ -33,9 +36,10 @@ class StaticGrid(IStrategy):
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Grid Entry Logic:
-        - Buy at each grid level when price drops to it
-        - Grid levels: 1500, 1550, 1600, ..., 4500 (step 50)
+        Dynamic Grid Entry Logic:
+        - Automatically calculates grid levels based on current price
+        - Creates grid points with fixed percentage step (e.g., 1.5% apart)
+        - Works for any price range (BTC, ETH, SOL, etc.)
         """
         dataframe['enter_long'] = False
         
@@ -46,19 +50,27 @@ class StaticGrid(IStrategy):
         current_price = dataframe['close'].iloc[-1]
         print(f"[StaticGrid] Current price: {current_price:.2f}", file=sys.stderr, flush=True)
         
-        # Check which grid level price is closest to or below
-        # If price is near any grid level, buy
-        for i in range(int((self.UPPER - self.LOWER) / self.STEP) + 1):
-            grid_price = self.LOWER + i * self.STEP
+        # Dynamic grid: use percentage-based steps instead of fixed price steps
+        # Grid step: 1.5% (you can adjust this)
+        grid_step_pct = 0.015  # 1.5%
+        
+        # Create grid levels around current price
+        # Generate grid points below current price (for buying)
+        num_levels_below = 10  # Generate 10 levels below current price
+        
+        for i in range(num_levels_below):
+            grid_price = current_price * ((1 - grid_step_pct) ** (i + 1))
             
-            # Buy if we're at this grid level (within 2% tolerance)
-            # This allows buying at intermediate prices too
-            if current_price <= grid_price * 1.02:
-                # Mark this candle and recent candles for entry
-                dataframe.loc[dataframe['close'] <= grid_price * 1.02, 'enter_long'] = True
+            # Buy if price drops to or below this grid level
+            if dataframe['low'].iloc[-1] <= grid_price * 1.01:
+                dataframe['enter_long'] = True
+                print(f"[StaticGrid] Buy signal at grid level {i+1}: {grid_price:.2f}", file=sys.stderr, flush=True)
+                break  # Only buy at one level per candle
         
         entries = dataframe['enter_long'].sum()
-        print(f"[StaticGrid] Entry signals: {entries}", file=sys.stderr, flush=True)
+        if entries > 0:
+            print(f"[StaticGrid] Entry signals: {entries}", file=sys.stderr, flush=True)
+        
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: