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added plots; fixes

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This commit is contained in:
Dobromir Popov 2025-03-17 02:46:33 +02:00
parent d9d0ba9da8
commit 469d681c4b
2 changed files with 308 additions and 132 deletions
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43
crypto/gogo2/.vscode/launch.json vendored
View File

@ -24,18 +24,53 @@
"type": "python", "type": "python",
"request": "launch", "request": "launch",
"program": "main.py", "program": "main.py",
"args": ["--mode", "live", "--demo"], "args": [
"--mode", "live",
"--demo", "true",
"--symbol", "ETH/USDT",
"--timeframe", "1m"
],
"console": "integratedTerminal", "console": "integratedTerminal",
"justMyCode": true "justMyCode": true,
"env": {
"PYTHONUNBUFFERED": "1"
}
}, },
{ {
"name": "Live Trading (Real)", "name": "Live Trading (Real)",
"type": "python", "type": "python",
"request": "launch", "request": "launch",
"program": "main.py", "program": "main.py",
"args": ["--mode", "live"], "args": [
"--mode", "live",
"--demo", "false",
"--symbol", "ETH/USDT",
"--timeframe", "1m",
"--leverage", "50"
],
"console": "integratedTerminal", "console": "integratedTerminal",
"justMyCode": true "justMyCode": true,
"env": {
"PYTHONUNBUFFERED": "1"
}
},
{
"name": "Live Trading (BTC Futures)",
"type": "python",
"request": "launch",
"program": "main.py",
"args": [
"--mode", "live",
"--demo", "false",
"--symbol", "BTC/USDT",
"--timeframe", "5m",
"--leverage", "20"
],
"console": "integratedTerminal",
"justMyCode": true,
"env": {
"PYTHONUNBUFFERED": "1"
}
} }
] ]
} }

371
crypto/gogo2/main.py
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@ -26,6 +26,8 @@ import io
import matplotlib.dates as mdates import matplotlib.dates as mdates
from matplotlib.figure import Figure from matplotlib.figure import Figure
from PIL import Image from PIL import Image
import matplotlib.pyplot as mpf
import matplotlib.gridspec as gridspec
# Configure logging # Configure logging
logging.basicConfig( logging.basicConfig(
@ -1782,35 +1784,34 @@ class Agent:
return False return False
def add_chart_to_tensorboard(self, env, global_step): def add_chart_to_tensorboard(self, env, global_step):
"""Add enhanced trading chart to TensorBoard""" """Add trading chart to TensorBoard"""
if len(env.data) < 10: # Minimum data to show
return
try: try:
# Create chart with annotations # Create chart image
chart_img = create_candlestick_figure( chart_img = create_candlestick_figure(
env.data, env.data,
env.trade_signals, env.trade_signals,
window_size=100, window_size=100,
title=f"Trading Chart (Step {global_step})" title=f"Trading Chart - Step {global_step}"
) )
# Add to TensorBoard if chart_img is not None:
self.writer.add_image('Trading Chart', np.array(chart_img).transpose(2, 0, 1), global_step) # Convert PIL image to numpy array for TensorBoard
self.chart_step = global_step chart_array = np.array(chart_img)
# TensorBoard expects [C, H, W] format
# Also log position information chart_array = np.transpose(chart_array, (2, 0, 1))
if env.position != 'flat': self.writer.add_image('Trading Chart', chart_array, global_step)
position_info = {
'position_type': env.position,
'entry_price': env.entry_price,
'position_size': env.position_size,
'unrealized_pnl': env.total_pnl
}
self.writer.add_text('Position', str(position_info), global_step)
# Add position information as text
position_info = f"""
**Current Position**: {env.position.upper()}
**Entry Price**: ${env.entry_price:.2f if env.entry_price else 0:.2f}
**Current Price**: ${env.data[-1]['close']:.2f}
**Position Size**: ${env.position_size:.2f}
**Unrealized PnL**: ${env.unrealized_pnl:.2f}
"""
self.writer.add_text('Position', position_info, global_step)
except Exception as e: except Exception as e:
logger.error(f"Error creating chart: {e}") logger.error(f"Error adding chart to TensorBoard: {str(e)}")
async def get_live_prices(symbol="ETH/USDT", timeframe="1m"): async def get_live_prices(symbol="ETH/USDT", timeframe="1m"):
"""Get live price data using websockets""" """Get live price data using websockets"""
@ -2225,122 +2226,155 @@ async def get_historical_data(exchange, symbol="ETH/USDT", timeframe="1m", limit
logger.error(f"Failed to fetch historical data: {e}") logger.error(f"Failed to fetch historical data: {e}")
return [] return []
async def live_trading(agent, env, exchange, demo=True): async def live_trading(agent, env, exchange, symbol="ETH/USDT", timeframe="1m", demo=True, leverage=50):
"""Run live trading with the trained agent""" """
logger.info(f"Starting live trading (demo mode: {demo})") Run the trading bot in live mode with enhanced error handling and monitoring
try: Args:
# Subscribe to websocket for real-time data agent: Trained trading agent
symbol = "ETH/USDT" env: Trading environment
timeframe = "1m" exchange: CCXT exchange instance
symbol: Trading pair (default: ETH/USDT)
timeframe: Candle timeframe (default: 1m)
demo: If True, simulate trades without executing (default: True)
leverage: Leverage for futures trading (default: 50x)
"""
logger.info(f"Starting live trading for {symbol} on {timeframe} timeframe")
logger.info(f"Mode: {'DEMO (paper trading)' if demo else 'LIVE TRADING'}")
# Initialize with historical data if not demo:
success = await env.fetch_initial_data(exchange, symbol, timeframe, 100) # Confirm with user before starting live trading
if not success: confirmation = input(f"⚠️ WARNING: You are about to start LIVE TRADING with real funds on {symbol}. Type 'CONFIRM' to continue: ")
logger.error("Failed to initialize with historical data") if confirmation != "CONFIRM":
logger.info("Live trading canceled by user")
return return
# Main trading loop # Initialize futures trading if not in demo mode
try:
await env.initialize_futures(exchange)
logger.info(f"Futures trading initialized with {leverage}x leverage")
except Exception as e:
logger.error(f"Failed to initialize futures trading: {str(e)}")
logger.info("Falling back to demo mode for safety")
demo = True
# Initialize TensorBoard for monitoring
if not hasattr(agent, 'writer') or agent.writer is None:
agent.writer = SummaryWriter(f'runs/live_{symbol.replace("/", "_")}_{datetime.now().strftime("%Y%m%d_%H%M%S")}')
# Track performance metrics
trades_count = 0
winning_trades = 0
total_profit = 0
max_drawdown = 0
peak_balance = env.balance
step_counter = 0 step_counter = 0
prev_position = 'flat'
# For online learning # Create directory for trade logs
states = [] os.makedirs('trade_logs', exist_ok=True)
actions = [] trade_log_path = f'trade_logs/trades_{datetime.now().strftime("%Y%m%d_%H%M%S")}.csv'
rewards = [] with open(trade_log_path, 'w') as f:
next_states = [] f.write("timestamp,action,price,position_size,balance,pnl\n")
dones = []
try:
while True: while True:
# Wait for the next candle (1 minute) try:
await asyncio.sleep(5) # Check every 5 seconds # Fetch latest candle data
candle = await get_latest_candle(exchange, symbol)
# Fetch latest candle if candle is None:
latest_candle = await get_latest_candle(exchange, symbol) logger.warning("Failed to fetch latest candle, retrying in 5 seconds...")
await asyncio.sleep(5)
if not latest_candle:
logger.warning("No latest candle received, skipping update")
continue continue
# Update environment with new data # Add new data to environment
env.add_data(latest_candle) env.add_data(candle)
# Get current state # Get current state and select action
state = env.get_state() state = env.get_state()
# Select action (no exploration in live trading)
action = agent.select_action(state, training=False) action = agent.select_action(state, training=False)
# Take action # Execute action
next_state, reward, done = env.step(action) if not demo:
# Execute real trade on exchange
current_price = env.data[-1]['close']
trade_result = await env.execute_real_trade(exchange, action, current_price)
if not trade_result['success']:
logger.error(f"Trade execution failed: {trade_result['error']}")
# Continue with simulated trade for tracking purposes
# Store experience for online learning # Update environment with action (simulated in demo mode)
states.append(state) next_state, reward, done, info = env.step(action)
actions.append(action)
rewards.append(reward)
next_states.append(next_state)
dones.append(done)
# Online learning - update the model with new experiences # Log trade if position changed
if len(states) >= 10: # Batch size for online learning if env.position != prev_position:
# Store experiences in replay memory trades_count += 1
for i in range(len(states)): if env.last_trade_profit > 0:
agent.memory.push(states[i], actions[i], rewards[i], next_states[i], dones[i]) winning_trades += 1
total_profit += env.last_trade_profit
# Learn from experiences if we have enough samples # Log trade details
if len(agent.memory) > 32: with open(trade_log_path, 'a') as f:
loss = agent.learn() f.write(f"{datetime.now().isoformat()},{info['action']},{env.data[-1]['close']},{env.position_size},{env.balance},{env.last_trade_profit}\n")
if loss is not None:
agent.writer.add_scalar('Live/Loss', loss, step_counter)
# Clear the temporary storage logger.info(f"Trade executed: {info['action']} at ${env.data[-1]['close']:.2f}, PnL: ${env.last_trade_profit:.2f}")
states = []
actions = []
rewards = []
next_states = []
dones = []
# Save the updated model periodically # Update performance metrics
if step_counter % 100 == 0: if env.balance > peak_balance:
agent.save("models/trading_agent_live_updated.pt") peak_balance = env.balance
logger.info("Updated model saved during live trading") current_drawdown = (peak_balance - env.balance) / peak_balance if peak_balance > 0 else 0
if current_drawdown > max_drawdown:
max_drawdown = current_drawdown
# Log trading activity # Update TensorBoard metrics
action_names = ["HOLD", "BUY", "SELL", "CLOSE"]
logger.info(f"Price: ${latest_candle['close']:.2f} | Action: {action_names[action]}")
# Log performance metrics
if env.trades:
wins = sum(1 for t in env.trades if t.get('pnl_percent', 0) > 0)
win_rate = wins / len(env.trades) * 100
total_pnl = sum(t.get('pnl_dollar', 0) for t 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}")
# Analyze recent trades
trade_analysis = env.analyze_trades()
if trade_analysis:
logger.info(f"Recent Performance: Win Rate={trade_analysis.get('uptrend_win_rate', 0):.1f}% in uptrends, "
f"{trade_analysis.get('downtrend_win_rate', 0):.1f}% in downtrends")
# Add chart to TensorBoard periodically
step_counter += 1 step_counter += 1
if step_counter % 10 == 0: # Update chart every 10 steps agent.writer.add_scalar('Live/Balance', env.balance, step_counter)
agent.writer.add_scalar('Live/PnL', env.total_pnl, step_counter)
agent.writer.add_scalar('Live/Drawdown', current_drawdown * 100, step_counter)
# Update chart visualization
if step_counter % 5 == 0 or env.position != prev_position:
agent.add_chart_to_tensorboard(env, step_counter) agent.add_chart_to_tensorboard(env, step_counter)
# Also log current PnL and balance # Log performance summary
agent.writer.add_scalar('Live/Balance', env.balance, step_counter) if trades_count > 0:
agent.writer.add_scalar('Live/TotalPnL', env.total_pnl, step_counter) win_rate = (winning_trades / trades_count) * 100
agent.writer.add_scalar('Live/WinRate', agent.writer.add_scalar('Live/WinRate', win_rate, step_counter)
(env.win_count / (env.win_count + env.loss_count) * 100)
if (env.win_count + env.loss_count) > 0 else 0, performance_text = f"""
step_counter) **Live Trading Performance**
Balance: ${env.balance:.2f}
Total PnL: ${env.total_pnl:.2f}
Trades: {trades_count}
Win Rate: {win_rate:.1f}%
Max Drawdown: {max_drawdown*100:.1f}%
"""
agent.writer.add_text('Performance', performance_text, step_counter)
prev_position = env.position
# Wait for next candle
await asyncio.sleep(10) # Check every 10 seconds
except Exception as e:
logger.error(f"Error in live trading loop: {str(e)}")
logger.error(traceback.format_exc())
logger.info("Continuing after error...")
await asyncio.sleep(30) # Wait longer after an error
except KeyboardInterrupt: except KeyboardInterrupt:
logger.info("Live trading stopped by user") logger.info("Live trading stopped by user")
except Exception as e:
logger.error(f"Error in live trading: {e}") # Final performance report
raise if trades_count > 0:
win_rate = (winning_trades / trades_count) * 100
logger.info(f"Trading session summary:")
logger.info(f"Total trades: {trades_count}")
logger.info(f"Win rate: {win_rate:.1f}%")
logger.info(f"Final balance: ${env.balance:.2f}")
logger.info(f"Total profit: ${total_profit:.2f}")
logger.info(f"Maximum drawdown: {max_drawdown*100:.1f}%")
logger.info(f"Trade log saved to: {trade_log_path}")
async def get_latest_candle(exchange, symbol): async def get_latest_candle(exchange, symbol):
"""Get the latest candle data""" """Get the latest candle data"""
@ -2404,6 +2438,12 @@ async def main():
help='Mode to run the bot in') help='Mode to run the bot in')
parser.add_argument('--episodes', type=int, default=1000, help='Number of episodes to train') parser.add_argument('--episodes', type=int, default=1000, help='Number of episodes to train')
parser.add_argument('--demo', action='store_true', help='Run in demo mode (no real trades)') parser.add_argument('--demo', action='store_true', help='Run in demo mode (no real trades)')
parser.add_argument('--live', action='store_true', help='Run in live trading mode')
parser.add_argument('--real', action='store_true', help='Execute real trades (default is demo/paper trading)')
parser.add_argument('--symbol', type=str, default='ETH/USDT', help='Trading pair symbol')
parser.add_argument('--timeframe', type=str, default='1m', help='Candle timeframe')
parser.add_argument('--leverage', type=int, default=50, help='Leverage for futures trading')
parser.add_argument('--model', type=str, help='Path to model file')
args = parser.parse_args() args = parser.parse_args()
# Get device (GPU or CPU) # Get device (GPU or CPU)
@ -2438,12 +2478,46 @@ async def main():
avg_reward, avg_profit, win_rate = evaluate_agent(agent, env) avg_reward, avg_profit, win_rate = evaluate_agent(agent, env)
elif args.mode == 'live': elif args.mode == 'live':
# Load trained model # Add these arguments to the parser
agent.load("models/trading_agent_best_pnl.pt") parser.add_argument('--live', action='store_true', help='Run in live trading mode')
parser.add_argument('--real', action='store_true', help='Execute real trades (default is demo/paper trading)')
parser.add_argument('--symbol', type=str, default='ETH/USDT', help='Trading pair symbol')
parser.add_argument('--timeframe', type=str, default='1m', help='Candle timeframe')
parser.add_argument('--leverage', type=int, default=50, help='Leverage for futures trading')
parser.add_argument('--model', type=str, help='Path to model file')
args = parser.parse_args()
# Run live trading # In the main function, add this section to handle live trading
logger.info("Starting live trading...") if args.live:
await live_trading(agent, env, exchange, demo=args.demo) # Initialize exchange
exchange = await initialize_exchange()
# Load the trained agent
model_path = args.model if args.model else "models/trading_agent.pt"
if not os.path.exists(model_path):
logger.error(f"Model file not found: {model_path}")
return
# Initialize environment with historical data
env = TradingEnvironment(initial_balance=INITIAL_BALANCE, window_size=WINDOW_SIZE, demo=not args.real)
await env.fetch_initial_data(exchange, symbol=args.symbol, timeframe=args.timeframe)
# Initialize agent
state_size = env.get_state().shape[0]
agent = Agent(state_size=state_size, action_size=3)
agent.load(model_path)
logger.info(f"Loaded model from {model_path}")
# Start live trading
await live_trading(
agent=agent,
env=env,
exchange=exchange,
symbol=args.symbol,
timeframe=args.timeframe,
demo=not args.real,
leverage=args.leverage
)
finally: finally:
# Clean up exchange connection - safely close if possible # Clean up exchange connection - safely close if possible
@ -2458,6 +2532,73 @@ async def main():
except Exception as e: except Exception as e:
logger.warning(f"Could not properly close exchange connection: {e}") logger.warning(f"Could not properly close exchange connection: {e}")
# Add this function near the top with other utility functions
def create_candlestick_figure(data, trade_signals, window_size=100, title=""):
"""Create a candlestick chart with trade signals for TensorBoard visualization"""
if len(data) < 10:
return None
try:
# Create figure
fig = plt.figure(figsize=(12, 8))
# Prepare data for plotting
df = pd.DataFrame(data[-window_size:])
df['date'] = pd.to_datetime(df['timestamp'], unit='ms')
df.set_index('date', inplace=True)
# Create subplot grid
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
price_ax = plt.subplot(gs[0])
volume_ax = plt.subplot(gs[1], sharex=price_ax)
# Plot candlesticks
mpf.plot(df, type='candle', style='yahoo', ax=price_ax, volume=volume_ax)
# Add trade signals
for signal in trade_signals:
if signal['timestamp'] not in df.index:
continue
timestamp = pd.to_datetime(signal['timestamp'], unit='ms')
price = signal['price']
if signal['type'] == 'buy':
price_ax.plot(timestamp, price, '^', color='green', markersize=10, label='Buy')
elif signal['type'] == 'sell':
price_ax.plot(timestamp, price, 'v', color='red', markersize=10, label='Sell')
elif signal['type'] == 'close_long':
price_ax.plot(timestamp, price, 'x', color='gold', markersize=10, label='Close Long')
elif signal['type'] == 'close_short':
price_ax.plot(timestamp, price, 'x', color='black', markersize=10, label='Close Short')
elif 'stop_loss' in signal['type']:
price_ax.plot(timestamp, price, 'X', color='purple', markersize=10, label='Stop Loss')
elif 'take_profit' in signal['type']:
price_ax.plot(timestamp, price, '*', color='cyan', markersize=10, label='Take Profit')
# Add balance and PnL annotation
if trade_signals and 'balance' in trade_signals[-1] and 'pnl' in trade_signals[-1]:
balance = trade_signals[-1]['balance']
pnl = trade_signals[-1]['pnl']
price_ax.annotate(f"Balance: ${balance:.2f}\nPnL: ${pnl:.2f}",
xy=(0.02, 0.95), xycoords='axes fraction',
bbox=dict(boxstyle="round,pad=0.3", fc="white", ec="gray", alpha=0.8))
# Set title and format
price_ax.set_title(title)
fig.tight_layout()
# Convert to image
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
img = Image.open(buf)
return img
except Exception as e:
logger.error(f"Error creating chart: {str(e)}")
return None
if __name__ == "__main__": if __name__ == "__main__":
try: try:
asyncio.run(main()) asyncio.run(main())