fixes
This commit is contained in:
Dobromir Popov
parent
6f78703ba1
commit
cfddc996d7
@ -1703,6 +1703,7 @@ async def train_agent(agent, env, num_episodes=1000, max_steps_per_episode=1000,
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]
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current_stage = 0
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# Initialize stats dictionary with the correct keys
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stats = {
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'episode_rewards': [],
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'episode_profits': [],
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@ -2070,9 +2071,9 @@ def plot_training_results(stats):
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plt.xlabel('Episode')
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plt.ylabel('Reward')
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# Plot balance
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# Plot balance/profits
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plt.subplot(3, 2, 2)
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plt.plot(stats['episode_profits']) # Changed from 'episode_pnls'
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plt.plot(stats['episode_profits'])
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plt.title('Episode Profits')
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plt.xlabel('Episode')
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plt.ylabel('Profit ($)')
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@ -2086,14 +2087,14 @@ def plot_training_results(stats):
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# Plot trade count
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plt.subplot(3, 2, 4)
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plt.plot(stats['trade_counts']) # Changed from 'episode_lengths'
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plt.plot(stats['trade_counts'])
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plt.title('Number of Trades')
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plt.xlabel('Episode')
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plt.ylabel('Trades')
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# Plot prediction accuracy
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plt.subplot(3, 2, 5)
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plt.plot(stats['prediction_accuracies']) # Changed from 'prediction_accuracy'
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plt.plot(stats['prediction_accuracies'])
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plt.title('Prediction Accuracy')
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plt.xlabel('Episode')
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plt.ylabel('Accuracy (%)')
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@ -2103,6 +2104,8 @@ def plot_training_results(stats):
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plt.savefig('training_results.png')
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plt.close()
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logger.info("Training results saved to training_results.png")
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def evaluate_agent(agent, env, num_episodes=10):
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"""Evaluate the agent on test data"""
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total_reward = 0
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@ -2385,8 +2388,9 @@ async def fetch_ohlcv_data(exchange, symbol, timeframe, limit):
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async def main():
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"""Main function to run the trading bot"""
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parser = argparse.ArgumentParser(description='Crypto Trading Bot')
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parser.add_argument('--mode', type=str, default='train', choices=['train', 'evaluate', 'live'],
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help='Mode to run the bot in')
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parser.add_argument('--mode', type=str, default='train',
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choices=['train', 'evaluate', 'live', 'continuous'],
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help='Mode to run the bot in (train, evaluate, live, or continuous)')
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parser.add_argument('--episodes', type=int, default=1000, help='Number of episodes to train')
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parser.add_argument('--demo', action='store_true', help='Run in demo mode (no real trades)')
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parser.add_argument('--refresh-data', action='store_true', help='Refresh data during training')
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@ -2398,56 +2402,104 @@ async def main():
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device = get_device(args.device)
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exchange = None
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try:
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# Initialize exchange
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exchange = await initialize_exchange()
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# Create environment
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env = TradingEnvironment(initial_balance=INITIAL_BALANCE, window_size=30, demo=args.demo)
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exchange_id = 'mexc'
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exchange_class = getattr(ccxt, exchange_id)
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exchange = exchange_class({
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'apiKey': MEXC_API_KEY,
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'secret': MEXC_SECRET_KEY,
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'enableRateLimit': True,
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'options': {
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'defaultType': 'future',
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}
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})
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logger.info(f"Exchange initialized with standard CCXT: {exchange.id}")
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# Fetch initial data
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await env.fetch_initial_data(exchange, "ETH/USDT", "1m", 1000)
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logger.info("Fetching initial data for ETH/USDT")
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data = await fetch_ohlcv_data(exchange, "ETH/USDT", "1m", 500)
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# Create agent
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# Initialize environment
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env = TradingEnvironment(
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data=data,
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symbol="ETH/USDT",
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timeframe="1m",
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leverage=MAX_LEVERAGE,
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initial_balance=INITIAL_BALANCE,
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is_demo=args.demo or args.mode != 'live'
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)
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logger.info(f"Initialized environment with {len(data)} candles")
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# Initialize agent
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agent = Agent(STATE_SIZE, 4, hidden_size=384, lstm_layers=2, attention_heads=4, device=device)
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if args.mode == 'train':
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# Train the agent
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logger.info(f"Starting training for {args.episodes} episodes...")
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stats = await train_agent(agent, env, num_episodes=args.episodes, exchange=exchange)
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# Pass exchange to training function if refresh-data is enabled
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if args.refresh_data:
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logger.info("Data refresh enabled during training")
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stats = await train_agent(agent, env, num_episodes=args.episodes, exchange=exchange)
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else:
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stats = await train_agent(agent, env, num_episodes=args.episodes)
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elif args.mode == 'continuous':
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# Run in continuous mode - train indefinitely
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logger.info("Starting continuous training mode. Press Ctrl+C to stop.")
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episode_counter = 0
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try:
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while True: # Run indefinitely until manually stopped
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# Train for a batch of episodes
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batch_size = 50 # Train in batches of 50 episodes
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logger.info(f"Starting training batch {episode_counter // batch_size + 1}")
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# Refresh data at the start of each batch
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if exchange:
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logger.info("Refreshing data for new training batch")
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new_data = await fetch_ohlcv_data(exchange, "ETH/USDT", "1m", 500)
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if new_data:
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# Replace environment data with fresh data
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env.data = new_data
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env.reset()
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logger.info(f"Updated environment with {len(new_data)} fresh candles")
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# Train for a batch of episodes
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stats = await train_agent(agent, env, num_episodes=batch_size, exchange=exchange)
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# Save model after each batch
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agent.save(f"models/trading_agent_continuous_{episode_counter}.pt")
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# Increment counter
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episode_counter += batch_size
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# Sleep briefly to prevent excessive API calls
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await asyncio.sleep(5)
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except KeyboardInterrupt:
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logger.info("Continuous training stopped by user")
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# Save final model
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agent.save("models/trading_agent_continuous_final.pt")
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logger.info("Final model saved")
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elif args.mode == 'evaluate':
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# Load trained model
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# Load the best model
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agent.load("models/trading_agent_best_pnl.pt")
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# Evaluate the agent
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logger.info("Evaluating agent...")
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avg_reward, avg_profit, win_rate = evaluate_agent(agent, env)
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results = evaluate_agent(agent, env, num_episodes=10)
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logger.info(f"Evaluation results: {results}")
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elif args.mode == 'live':
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# Load trained model
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# Load the best model
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agent.load("models/trading_agent_best_pnl.pt")
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# Run live trading
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logger.info("Starting live trading...")
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await live_trading(agent, env, exchange, demo=args.demo)
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await live_trading(agent, env, exchange)
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except Exception as e:
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logger.error(f"Error: {e}")
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logger.error(f"Traceback: {traceback.format_exc()}")
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finally:
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# Clean up exchange connection - safely close if possible
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# Close exchange connection
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if exchange:
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try:
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# Some CCXT exchanges have close method, others don't
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if hasattr(exchange, 'close'):
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await exchange.close()
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elif hasattr(exchange, 'client') and hasattr(exchange.client, 'close'):
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await exchange.client.close()
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await exchange.client.close()
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logger.info("Exchange connection closed")
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except Exception as e:
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logger.warning(f"Could not properly close exchange connection: {e}")
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@ -7,3 +7,4 @@ ccxt>=2.0.0
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websockets>=10.0
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tensorboard>=2.6.0
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scikit-learn
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mplfinance
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