排查加log+2

config_examples/staticgrid.json

@@ -5,6 +5,7 @@
   "stake_amount": 40,
   "tradable_balance_ratio": 0.99,
   "dry_run": false,
+  "dry_run_wallet": 10000,
   "timeframe": "1h",
   "position_adjustment_enable": true,
   "process_only_new_candles": true,

freqtrade/templates/staticgrid.py

@@ -33,34 +33,55 @@ class StaticGrid(IStrategy):
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Aggressive entry: Always buy at current price if we don't have a position yet.
-        The grid logic will be handled by custom_stake_amount and max_open_trades.
+        Grid Entry Logic:
+        - Buy at each grid level when price drops to it
+        - Grid levels: 1500, 1550, 1600, ..., 4500 (step 50)
         """
-        print(f"[StaticGrid] populate_entry_trend called! df len: {len(dataframe)}", file=sys.stderr, flush=True)
+        dataframe['enter_long'] = False
         
-        # EXTREME: Always set buy signal for every candle
-        # This forces the bot to try to enter on every opportunity
-        dataframe['enter_long'] = True
+        if len(dataframe) == 0:
+            return dataframe
         
-        if len(dataframe) > 0:
-            latest_close = dataframe['close'].iloc[-1]
-            latest_low = dataframe['low'].iloc[-1]
-            print(f"[StaticGrid] Latest close: {latest_close:.2f}, low: {latest_low:.2f}", file=sys.stderr, flush=True)
-            print(f"[StaticGrid] Forcing entry on ALL candles!", file=sys.stderr, flush=True)
+        # Get current price
+        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
+            
+            # 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
+        
+        entries = dataframe['enter_long'].sum()
+        print(f"[StaticGrid] Entry signals: {entries}", file=sys.stderr, flush=True)
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Exit logic: Sell when price bounces back (any upward movement = profit)
+        Exit Logic:
+        - Exit when price rises 1% (small profit per grid level)
+        - This matches the grid step of 50 points (~1.6% at 3000 price)
         """
-        print(f"[StaticGrid] populate_exit_trend called! df len: {len(dataframe)}", file=sys.stderr, flush=True)
+        dataframe['exit_long'] = False
         
-        dataframe['exit_long'] = (dataframe['close'] > dataframe['close'].shift(1)).fillna(False)
+        if len(dataframe) < 2:
+            return dataframe
         
-        exit_count = dataframe['exit_long'].sum()
-        if exit_count > 0:
-            print(f"[StaticGrid] Exit signals: {exit_count}", file=sys.stderr, flush=True)
+        # Exit when price goes up 1% from entry
+        # (Freqtrade tracks entry price internally)
+        dataframe.loc[
+            dataframe['close'] > dataframe['open'] * 1.01,
+            'exit_long'
+        ] = True
+        
+        exits = dataframe['exit_long'].sum()
+        if exits > 0:
+            print(f"[StaticGrid] Exit signals: {exits}", file=sys.stderr, flush=True)
         
         return dataframe