edits

crypto/gogo2/main.py

@@ -875,76 +875,83 @@ async def main():
     parser.add_argument('--demo', action='store_true', help='Run in demo mode (no real trading)')
     args = parser.parse_args()
     
-    # Initialize exchange
+    # Initialize exchange with async support
-    exchange = ccxt.mexc({
+    exchange_id = 'mexc'
+    exchange_class = getattr(ccxt.async_support, exchange_id)
+    exchange = exchange_class({
         'apiKey': MEXC_API_KEY,
         'secret': MEXC_SECRET_KEY,
         'enableRateLimit': True,
     })
     
-    # Create environment
+    try:
-    env = TradingEnvironment(
+        # Create environment
-        exchange=exchange,
+        env = TradingEnvironment(
-        symbol="ETH/USDT",
+            exchange=exchange,
-        timeframe="1m",
+            symbol="ETH/USDT",
-        leverage=MAX_LEVERAGE,
+            timeframe="1m",
-        initial_balance=INITIAL_BALANCE,
+            leverage=MAX_LEVERAGE,
-        is_demo=args.demo or args.mode != 'live'  # Only trade for real in live mode
+            initial_balance=INITIAL_BALANCE,
-    )
+            is_demo=args.demo or args.mode != 'live'
-    
+        )
-    # Fetch initial data
-    await env.fetch_initial_data()
-    
-    # Create agent
-    agent = Agent(state_size=STATE_SIZE, action_size=env.action_space)
-    
-    # Try to load existing model
-    model_loaded = agent.load()
-    if not model_loaded and args.mode in ['eval', 'live']:
-        logger.warning("No pre-trained model found. Consider training first!")
-    
-    if args.mode == 'train':
-        # Training mode
-        logger.info("Starting training mode")
-        await train_agent(agent, env, num_episodes=args.episodes)
         
-    elif args.mode == 'eval':
+        # Fetch initial data
-        # Evaluation mode
+        await env.fetch_initial_data()
-        logger.info("Starting evaluation mode")
-        eval_reward, eval_profit, win_rate = evaluate_agent(agent, env, num_episodes=args.episodes)
         
-    elif args.mode == 'live':
+        # Create agent
-        # Live trading mode
+        agent = Agent(state_size=STATE_SIZE, action_size=env.action_space)
-        logger.info("Starting live trading mode with real-time data")
-        logger.info(f"Demo mode: {args.demo}")
         
-        # Live trading loop
+        # Try to load existing model
-        async for candle in get_live_prices("ETH/USDT", "1m"):
+        model_loaded = agent.load()
-            # Update environment with new data
+        if not model_loaded and args.mode in ['eval', 'live']:
-            await env._update_with_new_data(candle)
+            logger.warning("No pre-trained model found. Consider training first!")
+        
+        if args.mode == 'train':
+            # Training mode
+            logger.info("Starting training mode")
+            await train_agent(agent, env, num_episodes=args.episodes)
             
-            # Only trade if we have enough data
+        elif args.mode == 'eval':
-            if len(env.data) >= env.window_size:
+            # Evaluation mode
-                # Get current state
+            logger.info("Starting evaluation mode")
-                state = env.get_state()
+            eval_reward, eval_profit, win_rate = evaluate_agent(agent, env, num_episodes=args.episodes)
+            
+        elif args.mode == 'live':
+            # Live trading mode
+            logger.info("Starting live trading mode with real-time data")
+            logger.info(f"Demo mode: {args.demo}")
+            
+            # Live trading loop
+            async for candle in get_live_prices("ETH/USDT", "1m"):
+                # Update environment with new data
+                await env._update_with_new_data(candle)
                 
-                # Select action (no exploration in live trading)
+                # Only trade if we have enough data
-                action = agent.select_action(state, training=False)
+                if len(env.data) >= env.window_size:
-                
+                    # Get current state
-                # Convert action number to readable format
+                    state = env.get_state()
-                action_names = ["HOLD", "BUY", "SELL", "CLOSE"]
+                    
-                logger.info(f"Price: ${candle['close']:.2f} | Action: {action_names[action]}")
+                    # Select action (no exploration in live trading)
-                
+                    action = agent.select_action(state, training=False)
-                # Take action
+                    
-                _, reward, _ = env.step(action)
+                    # Convert action number to readable format
-                
+                    action_names = ["HOLD", "BUY", "SELL", "CLOSE"]
-                # Print statistics
+                    logger.info(f"Price: ${candle['close']:.2f} | Action: {action_names[action]}")
-                if len(env.trades) > 0:
+                    
-                    wins = sum(1 for trade in env.trades if trade.get('pnl_percent', 0) > 0)
+                    # Take action
-                    win_rate = wins / len(env.trades) * 100
+                    _, reward, _ = env.step(action)
-                    total_pnl = sum(trade.get('pnl_dollar', 0) for trade in env.trades)
+                    
-                    logger.info(f"Balance: ${env.balance:.2f} | Trades: {len(env.trades)} | "
+                    # Print statistics
-                              f"Win Rate: {win_rate:.1f}% | Total PnL: ${total_pnl:.2f}")
+                    if len(env.trades) > 0:
+                        wins = sum(1 for trade in env.trades if trade.get('pnl_percent', 0) > 0)
+                        win_rate = wins / len(env.trades) * 100
+                        total_pnl = sum(trade.get('pnl_dollar', 0) for trade in env.trades)
+                        logger.info(f"Balance: ${env.balance:.2f} | Trades: {len(env.trades)} | "
+                                  f"Win Rate: {win_rate:.1f}% | Total PnL: ${total_pnl:.2f}")
+
+    finally:
+        # Clean up exchange connection
+        await exchange.close()
 
 if __name__ == "__main__":
     try:

crypto/gogo2/readme.md

@@ -66,12 +66,21 @@ python main.py --mode eval --episodes 10
 ### Live Trading Mode
 
 ```bash
-# Demo mode (no real trades)
+# Demo mode (simulated trading with real market data)
 python main.py --mode live --demo
-# Real trading
+
+# Real trading (actual trades on MEXC)
 python main.py --mode live
 ```
 
+Demo mode simulates trading using real-time market data but does not execute actual trades. It still:
+- Logs all trading decisions and performance metrics
+- Updates the model based on market data (if in training mode)
+- Displays real-time analytics and position information
+- Calculates theoretical profits/losses
+- Saves performance data to TensorBoard
+
+This makes it perfect for testing strategies without financial risk.
 
 ## Configuration
 

crypto/gogo2/trading_bot.log

@@ -0,0 +1,4 @@
+2025-03-10 10:31:19,097 - INFO - Fetching initial 60 candles for ETH/USDT...
+2025-03-10 10:31:24,545 - ERROR - Error fetching initial data: object list can't be used in 'await' expression
+2025-03-10 10:38:32,233 - INFO - Fetching initial 60 candles for ETH/USDT...
+2025-03-10 10:38:38,055 - ERROR - Error fetching initial data: object list can't be used in 'await' expression