added plots; fixes
This commit is contained in:
Dobromir Popov
parent
d9d0ba9da8
commit
469d681c4b
43
crypto/gogo2/.vscode/launch.json
vendored
43
crypto/gogo2/.vscode/launch.json
vendored
@ -24,18 +24,53 @@
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"type": "python",
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"request": "launch",
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"program": "main.py",
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"args": ["--mode", "live", "--demo"],
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"args": [
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"--mode", "live",
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"--demo", "true",
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"--symbol", "ETH/USDT",
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"--timeframe", "1m"
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],
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"console": "integratedTerminal",
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"justMyCode": true
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"justMyCode": true,
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"env": {
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"PYTHONUNBUFFERED": "1"
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}
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},
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{
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"name": "Live Trading (Real)",
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"type": "python",
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"request": "launch",
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"program": "main.py",
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"args": ["--mode", "live"],
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"args": [
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"--mode", "live",
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"--demo", "false",
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"--symbol", "ETH/USDT",
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"--timeframe", "1m",
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"--leverage", "50"
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],
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"console": "integratedTerminal",
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"justMyCode": true
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"justMyCode": true,
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"env": {
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"PYTHONUNBUFFERED": "1"
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}
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},
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{
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"name": "Live Trading (BTC Futures)",
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"type": "python",
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"request": "launch",
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"program": "main.py",
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"args": [
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"--mode", "live",
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"--demo", "false",
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"--symbol", "BTC/USDT",
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"--timeframe", "5m",
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"--leverage", "20"
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],
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"console": "integratedTerminal",
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"justMyCode": true,
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"env": {
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"PYTHONUNBUFFERED": "1"
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}
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}
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]
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}
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@ -26,6 +26,8 @@ import io
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import matplotlib.dates as mdates
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from matplotlib.figure import Figure
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from PIL import Image
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import matplotlib.pyplot as mpf
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import matplotlib.gridspec as gridspec
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# Configure logging
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logging.basicConfig(
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@ -1782,35 +1784,34 @@ class Agent:
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return False
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def add_chart_to_tensorboard(self, env, global_step):
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"""Add enhanced trading chart to TensorBoard"""
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if len(env.data) < 10: # Minimum data to show
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return
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"""Add trading chart to TensorBoard"""
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try:
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# Create chart with annotations
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# Create chart image
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chart_img = create_candlestick_figure(
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env.data,
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env.trade_signals,
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window_size=100,
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title=f"Trading Chart (Step {global_step})"
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title=f"Trading Chart - Step {global_step}"
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)
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# Add to TensorBoard
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self.writer.add_image('Trading Chart', np.array(chart_img).transpose(2, 0, 1), global_step)
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self.chart_step = global_step
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# Also log position information
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if env.position != 'flat':
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position_info = {
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'position_type': env.position,
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'entry_price': env.entry_price,
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'position_size': env.position_size,
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'unrealized_pnl': env.total_pnl
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}
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self.writer.add_text('Position', str(position_info), global_step)
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if chart_img is not None:
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# Convert PIL image to numpy array for TensorBoard
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chart_array = np.array(chart_img)
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# TensorBoard expects [C, H, W] format
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chart_array = np.transpose(chart_array, (2, 0, 1))
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self.writer.add_image('Trading Chart', chart_array, global_step)
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# Add position information as text
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position_info = f"""
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**Current Position**: {env.position.upper()}
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**Entry Price**: ${env.entry_price:.2f if env.entry_price else 0:.2f}
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**Current Price**: ${env.data[-1]['close']:.2f}
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**Position Size**: ${env.position_size:.2f}
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**Unrealized PnL**: ${env.unrealized_pnl:.2f}
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"""
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self.writer.add_text('Position', position_info, global_step)
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except Exception as e:
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logger.error(f"Error creating chart: {e}")
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logger.error(f"Error adding chart to TensorBoard: {str(e)}")
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async def get_live_prices(symbol="ETH/USDT", timeframe="1m"):
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"""Get live price data using websockets"""
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@ -2225,122 +2226,155 @@ async def get_historical_data(exchange, symbol="ETH/USDT", timeframe="1m", limit
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logger.error(f"Failed to fetch historical data: {e}")
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return []
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async def live_trading(agent, env, exchange, demo=True):
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"""Run live trading with the trained agent"""
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logger.info(f"Starting live trading (demo mode: {demo})")
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async def live_trading(agent, env, exchange, symbol="ETH/USDT", timeframe="1m", demo=True, leverage=50):
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"""
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Run the trading bot in live mode with enhanced error handling and monitoring
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try:
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# Subscribe to websocket for real-time data
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symbol = "ETH/USDT"
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timeframe = "1m"
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Args:
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agent: Trained trading agent
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env: Trading environment
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exchange: CCXT exchange instance
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symbol: Trading pair (default: ETH/USDT)
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timeframe: Candle timeframe (default: 1m)
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demo: If True, simulate trades without executing (default: True)
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leverage: Leverage for futures trading (default: 50x)
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"""
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logger.info(f"Starting live trading for {symbol} on {timeframe} timeframe")
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logger.info(f"Mode: {'DEMO (paper trading)' if demo else 'LIVE TRADING'}")
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# Initialize with historical data
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success = await env.fetch_initial_data(exchange, symbol, timeframe, 100)
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if not success:
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logger.error("Failed to initialize with historical data")
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if not demo:
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# Confirm with user before starting live trading
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confirmation = input(f"⚠️ WARNING: You are about to start LIVE TRADING with real funds on {symbol}. Type 'CONFIRM' to continue: ")
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if confirmation != "CONFIRM":
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logger.info("Live trading canceled by user")
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return
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# Main trading loop
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step_counter = 0
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# Initialize futures trading if not in demo mode
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try:
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await env.initialize_futures(exchange)
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logger.info(f"Futures trading initialized with {leverage}x leverage")
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except Exception as e:
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logger.error(f"Failed to initialize futures trading: {str(e)}")
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logger.info("Falling back to demo mode for safety")
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demo = True
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# For online learning
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states = []
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actions = []
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rewards = []
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next_states = []
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dones = []
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# Initialize TensorBoard for monitoring
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if not hasattr(agent, 'writer') or agent.writer is None:
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agent.writer = SummaryWriter(f'runs/live_{symbol.replace("/", "_")}_{datetime.now().strftime("%Y%m%d_%H%M%S")}')
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# Track performance metrics
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trades_count = 0
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winning_trades = 0
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total_profit = 0
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max_drawdown = 0
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peak_balance = env.balance
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step_counter = 0
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prev_position = 'flat'
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# Create directory for trade logs
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os.makedirs('trade_logs', exist_ok=True)
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trade_log_path = f'trade_logs/trades_{datetime.now().strftime("%Y%m%d_%H%M%S")}.csv'
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with open(trade_log_path, 'w') as f:
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f.write("timestamp,action,price,position_size,balance,pnl\n")
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try:
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while True:
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# Wait for the next candle (1 minute)
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await asyncio.sleep(5) # Check every 5 seconds
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try:
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# Fetch latest candle data
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candle = await get_latest_candle(exchange, symbol)
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if candle is None:
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logger.warning("Failed to fetch latest candle, retrying in 5 seconds...")
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await asyncio.sleep(5)
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continue
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# Fetch latest candle
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latest_candle = await get_latest_candle(exchange, symbol)
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# Add new data to environment
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env.add_data(candle)
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if not latest_candle:
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logger.warning("No latest candle received, skipping update")
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continue
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# Get current state and select action
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state = env.get_state()
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action = agent.select_action(state, training=False)
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# Update environment with new data
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env.add_data(latest_candle)
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# Execute action
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if not demo:
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# Execute real trade on exchange
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current_price = env.data[-1]['close']
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trade_result = await env.execute_real_trade(exchange, action, current_price)
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if not trade_result['success']:
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logger.error(f"Trade execution failed: {trade_result['error']}")
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# Continue with simulated trade for tracking purposes
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# Get current state
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state = env.get_state()
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# Update environment with action (simulated in demo mode)
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next_state, reward, done, info = env.step(action)
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# Select action (no exploration in live trading)
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action = agent.select_action(state, training=False)
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# Log trade if position changed
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if env.position != prev_position:
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trades_count += 1
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if env.last_trade_profit > 0:
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winning_trades += 1
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total_profit += env.last_trade_profit
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# Take action
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next_state, reward, done = env.step(action)
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# Log trade details
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with open(trade_log_path, 'a') as f:
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f.write(f"{datetime.now().isoformat()},{info['action']},{env.data[-1]['close']},{env.position_size},{env.balance},{env.last_trade_profit}\n")
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# Store experience for online learning
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states.append(state)
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actions.append(action)
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rewards.append(reward)
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next_states.append(next_state)
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dones.append(done)
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logger.info(f"Trade executed: {info['action']} at ${env.data[-1]['close']:.2f}, PnL: ${env.last_trade_profit:.2f}")
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# Online learning - update the model with new experiences
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if len(states) >= 10: # Batch size for online learning
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# Store experiences in replay memory
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for i in range(len(states)):
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agent.memory.push(states[i], actions[i], rewards[i], next_states[i], dones[i])
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# Update performance metrics
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if env.balance > peak_balance:
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peak_balance = env.balance
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current_drawdown = (peak_balance - env.balance) / peak_balance if peak_balance > 0 else 0
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if current_drawdown > max_drawdown:
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max_drawdown = current_drawdown
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# Learn from experiences if we have enough samples
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if len(agent.memory) > 32:
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loss = agent.learn()
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if loss is not None:
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agent.writer.add_scalar('Live/Loss', loss, step_counter)
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# Clear the temporary storage
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states = []
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actions = []
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rewards = []
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next_states = []
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dones = []
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# Save the updated model periodically
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if step_counter % 100 == 0:
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agent.save("models/trading_agent_live_updated.pt")
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logger.info("Updated model saved during live trading")
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# Log trading activity
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action_names = ["HOLD", "BUY", "SELL", "CLOSE"]
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logger.info(f"Price: ${latest_candle['close']:.2f} | Action: {action_names[action]}")
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# Log performance metrics
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if env.trades:
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wins = sum(1 for t in env.trades if t.get('pnl_percent', 0) > 0)
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win_rate = wins / len(env.trades) * 100
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total_pnl = sum(t.get('pnl_dollar', 0) for t in env.trades)
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logger.info(f"Balance: ${env.balance:.2f} | Trades: {len(env.trades)} | "
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f"Win Rate: {win_rate:.1f}% | Total PnL: ${total_pnl:.2f}")
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# Analyze recent trades
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trade_analysis = env.analyze_trades()
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if trade_analysis:
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logger.info(f"Recent Performance: Win Rate={trade_analysis.get('uptrend_win_rate', 0):.1f}% in uptrends, "
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f"{trade_analysis.get('downtrend_win_rate', 0):.1f}% in downtrends")
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# Add chart to TensorBoard periodically
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step_counter += 1
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if step_counter % 10 == 0: # Update chart every 10 steps
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agent.add_chart_to_tensorboard(env, step_counter)
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# Also log current PnL and balance
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# Update TensorBoard metrics
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step_counter += 1
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agent.writer.add_scalar('Live/Balance', env.balance, step_counter)
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agent.writer.add_scalar('Live/TotalPnL', env.total_pnl, step_counter)
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agent.writer.add_scalar('Live/WinRate',
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(env.win_count / (env.win_count + env.loss_count) * 100)
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if (env.win_count + env.loss_count) > 0 else 0,
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step_counter)
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agent.writer.add_scalar('Live/PnL', env.total_pnl, step_counter)
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agent.writer.add_scalar('Live/Drawdown', current_drawdown * 100, step_counter)
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# Update chart visualization
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if step_counter % 5 == 0 or env.position != prev_position:
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agent.add_chart_to_tensorboard(env, step_counter)
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# Log performance summary
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if trades_count > 0:
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win_rate = (winning_trades / trades_count) * 100
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agent.writer.add_scalar('Live/WinRate', win_rate, step_counter)
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performance_text = f"""
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**Live Trading Performance**
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Balance: ${env.balance:.2f}
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Total PnL: ${env.total_pnl:.2f}
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Trades: {trades_count}
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Win Rate: {win_rate:.1f}%
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Max Drawdown: {max_drawdown*100:.1f}%
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"""
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agent.writer.add_text('Performance', performance_text, step_counter)
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prev_position = env.position
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# Wait for next candle
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await asyncio.sleep(10) # Check every 10 seconds
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except Exception as e:
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logger.error(f"Error in live trading loop: {str(e)}")
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logger.error(traceback.format_exc())
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logger.info("Continuing after error...")
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await asyncio.sleep(30) # Wait longer after an error
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except KeyboardInterrupt:
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logger.info("Live trading stopped by user")
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except Exception as e:
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logger.error(f"Error in live trading: {e}")
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raise
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# Final performance report
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if trades_count > 0:
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win_rate = (winning_trades / trades_count) * 100
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logger.info(f"Trading session summary:")
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logger.info(f"Total trades: {trades_count}")
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logger.info(f"Win rate: {win_rate:.1f}%")
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logger.info(f"Final balance: ${env.balance:.2f}")
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logger.info(f"Total profit: ${total_profit:.2f}")
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logger.info(f"Maximum drawdown: {max_drawdown*100:.1f}%")
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logger.info(f"Trade log saved to: {trade_log_path}")
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async def get_latest_candle(exchange, symbol):
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"""Get the latest candle data"""
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@ -2404,6 +2438,12 @@ async def main():
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help='Mode to run the bot in')
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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('--live', action='store_true', help='Run in live trading mode')
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parser.add_argument('--real', action='store_true', help='Execute real trades (default is demo/paper trading)')
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parser.add_argument('--symbol', type=str, default='ETH/USDT', help='Trading pair symbol')
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parser.add_argument('--timeframe', type=str, default='1m', help='Candle timeframe')
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parser.add_argument('--leverage', type=int, default=50, help='Leverage for futures trading')
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parser.add_argument('--model', type=str, help='Path to model file')
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args = parser.parse_args()
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# Get device (GPU or CPU)
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@ -2438,12 +2478,46 @@ async def main():
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avg_reward, avg_profit, win_rate = evaluate_agent(agent, env)
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elif args.mode == 'live':
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# Load trained model
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agent.load("models/trading_agent_best_pnl.pt")
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# Add these arguments to the parser
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parser.add_argument('--live', action='store_true', help='Run in live trading mode')
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parser.add_argument('--real', action='store_true', help='Execute real trades (default is demo/paper trading)')
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parser.add_argument('--symbol', type=str, default='ETH/USDT', help='Trading pair symbol')
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parser.add_argument('--timeframe', type=str, default='1m', help='Candle timeframe')
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parser.add_argument('--leverage', type=int, default=50, help='Leverage for futures trading')
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parser.add_argument('--model', type=str, help='Path to model file')
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args = parser.parse_args()
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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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# In the main function, add this section to handle live trading
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if args.live:
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# Initialize exchange
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exchange = await initialize_exchange()
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# Load the trained agent
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model_path = args.model if args.model else "models/trading_agent.pt"
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if not os.path.exists(model_path):
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logger.error(f"Model file not found: {model_path}")
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return
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# Initialize environment with historical data
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env = TradingEnvironment(initial_balance=INITIAL_BALANCE, window_size=WINDOW_SIZE, demo=not args.real)
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await env.fetch_initial_data(exchange, symbol=args.symbol, timeframe=args.timeframe)
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# Initialize agent
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state_size = env.get_state().shape[0]
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agent = Agent(state_size=state_size, action_size=3)
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agent.load(model_path)
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logger.info(f"Loaded model from {model_path}")
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# Start live trading
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await live_trading(
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agent=agent,
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env=env,
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exchange=exchange,
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symbol=args.symbol,
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timeframe=args.timeframe,
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demo=not args.real,
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leverage=args.leverage
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)
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finally:
|
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# Clean up exchange connection - safely close if possible
|
||||
@ -2458,6 +2532,73 @@ async def main():
|
||||
except Exception as 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__":
|
||||
try:
|
||||
asyncio.run(main())
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user