added continious mode. fixed errors

crypto/gogo2/.vscode/launch.json

@@ -6,7 +6,7 @@
             "type": "python",
             "request": "launch",
             "program": "main.py",
-            "args": ["--mode", "train", "--episodes", "1000"],
+            "args": ["--mode", "train", "--episodes", "100"],
             "console": "integratedTerminal",
             "justMyCode": true
         },
@@ -36,6 +36,15 @@
             "args": ["--mode", "live"],
             "console": "integratedTerminal",
             "justMyCode": true
+        },
+        {
+            "name": "Continuous Training",
+            "type": "python",
+            "request": "launch",
+            "program": "main.py",
+            "args": ["--mode", "continuous", "--refresh-data"],
+            "console": "integratedTerminal",
+            "justMyCode": true
         }
     ]
 }

crypto/gogo2/main.py

@@ -1679,7 +1679,7 @@ async def get_live_prices(symbol="ETH/USDT", timeframe="1m"):
                 await asyncio.sleep(5)
                 break
 
-async def train_agent(agent, env, num_episodes=1000, max_steps_per_episode=1000, exchange=None):
+async def train_agent(agent, env, num_episodes=1000, max_steps_per_episode=1000, exchange=None, args=None):
     """Train the agent using historical and live data with GPU acceleration"""
     logger.info(f"Starting training on device: {agent.device}")
     
@@ -2404,39 +2404,26 @@ async def main():
     exchange = None
     try:
         # Initialize exchange
-        exchange_id = 'mexc'
-        exchange_class = getattr(ccxt, exchange_id)
-        exchange = exchange_class({
-            'apiKey': MEXC_API_KEY,
-            'secret': MEXC_SECRET_KEY,
-            'enableRateLimit': True,
-            'options': {
-                'defaultType': 'future',
-            }
-        })
-        logger.info(f"Exchange initialized with standard CCXT: {exchange.id}")
+        exchange = await initialize_exchange()
+        
+        # Create environment with the correct parameters
+        env = TradingEnvironment(
+            initial_balance=INITIAL_BALANCE, 
+            window_size=30, 
+            demo=args.demo or args.mode != 'live'
+        )
         
         # Fetch initial data
         logger.info("Fetching initial data for ETH/USDT")
-        data = await fetch_ohlcv_data(exchange, "ETH/USDT", "1m", 500)
-        
-        # Initialize environment
-        env = TradingEnvironment(
-            data=data,
-            symbol="ETH/USDT",
-            timeframe="1m",
-            leverage=MAX_LEVERAGE,
-            initial_balance=INITIAL_BALANCE,
-            is_demo=args.demo or args.mode != 'live'
-        )
-        logger.info(f"Initialized environment with {len(data)} candles")
+        await env.fetch_initial_data(exchange, "ETH/USDT", "1m", 500)
         
         # Initialize agent
         agent = Agent(STATE_SIZE, 4, hidden_size=384, lstm_layers=2, attention_heads=4, device=device)
         
         if args.mode == 'train':
             # Train the agent
-            stats = await train_agent(agent, env, num_episodes=args.episodes, exchange=exchange)
+            logger.info(f"Starting training for {args.episodes} episodes...")
+            stats = await train_agent(agent, env, num_episodes=args.episodes, exchange=exchange, args=args)
             
         elif args.mode == 'continuous':
             # Run in continuous mode - train indefinitely
@@ -2449,17 +2436,13 @@ async def main():
                     logger.info(f"Starting training batch {episode_counter // batch_size + 1}")
                     
                     # Refresh data at the start of each batch
-                    if exchange:
+                    if exchange and args.refresh_data:
                         logger.info("Refreshing data for new training batch")
-                        new_data = await fetch_ohlcv_data(exchange, "ETH/USDT", "1m", 500)
-                        if new_data:
-                            # Replace environment data with fresh data
-                            env.data = new_data
-                            env.reset()
-                            logger.info(f"Updated environment with {len(new_data)} fresh candles")
+                        await env.fetch_new_data(exchange, "ETH/USDT", "1m", 500)
+                        logger.info(f"Updated environment with fresh candles")
                     
                     # Train for a batch of episodes
-                    stats = await train_agent(agent, env, num_episodes=batch_size, exchange=exchange)
+                    stats = await train_agent(agent, env, num_episodes=args.episodes, exchange=exchange, args=args)
                     
                     # Save model after each batch
                     agent.save(f"models/trading_agent_continuous_{episode_counter}.pt")
@@ -2481,6 +2464,7 @@ async def main():
             agent.load("models/trading_agent_best_pnl.pt")
             
             # Evaluate the agent
+            logger.info("Evaluating agent...")
             results = evaluate_agent(agent, env, num_episodes=10)
             logger.info(f"Evaluation results: {results}")
             
@@ -2490,7 +2474,7 @@ async def main():
             
             # Run live trading
             logger.info("Starting live trading...")
-            await live_trading(agent, env, exchange)
+            await live_trading(agent, env, exchange, demo=args.demo)
         
     except Exception as e:
         logger.error(f"Error: {e}")
@@ -2499,6 +2483,10 @@ async def main():
         # Close exchange connection
         if exchange:
             try:
+                # Some CCXT exchanges have close method, others don't
+                if hasattr(exchange, 'close'):
+                    await exchange.close()
+                elif hasattr(exchange, 'client') and hasattr(exchange.client, 'close'):
                     await exchange.client.close()
                 logger.info("Exchange connection closed")
             except Exception as e: