plot charts
This commit is contained in:
Dobromir Popov
parent
715261a3f9
commit
6f78703ba1
@ -1 +1 @@
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{"best_reward": 202.7441047517104, "best_pnl": -1.285678343969877, "best_win_rate": 38.70967741935484, "last_episode": 20, "timestamp": "2025-03-10T13:31:02.938465"}
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{"best_reward": 202.7441047517104, "best_pnl": 0.25999080227362914, "best_win_rate": 44.44444444444444, "last_episode": 0, "timestamp": "2025-03-10T14:42:11.838854"}
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@ -23,6 +23,9 @@ import copy
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import argparse
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import traceback
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import math
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import matplotlib.dates as mdates
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from matplotlib.figure import Figure
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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# Configure logging
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logging.basicConfig(
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@ -1758,18 +1761,11 @@ async def train_agent(agent, env, num_episodes=1000, max_steps_per_episode=1000,
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# Set risk factor for this episode
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env.risk_factor = risk_factor
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# Refresh data with latest candles if exchange is provided
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if exchange is not None:
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try:
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logger.info(f"Fetching latest data for episode {episode}")
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latest_data = await fetch_ohlcv_data(exchange, "ETH/USDT", "1m", 100)
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if latest_data:
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# Add new data to environment
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for candle in latest_data:
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env.add_data(candle)
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logger.info(f"Added {len(latest_data)} new candles for episode {episode}")
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except Exception as e:
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logger.error(f"Error refreshing data: {e}")
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# Update training data if exchange is available
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if exchange and args.refresh_data:
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# Fetch new data at the start of each episode
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logger.info(f"Refreshing data for episode {episode}")
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await env.fetch_new_data(exchange, "ETH/USDT", "1m", 100)
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# Reset environment
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state = env.reset()
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@ -1796,6 +1792,59 @@ async def train_agent(agent, env, num_episodes=1000, max_steps_per_episode=1000,
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# Update price predictions
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env.update_price_predictions()
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# Log OHLCV data to TensorBoard at the start of the episode
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if episode % 5 == 0: # Log every 5 episodes to avoid too much data
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# Create a DataFrame from the environment's data
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df_ohlcv = pd.DataFrame([{
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'timestamp': candle['timestamp'],
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'open': candle['open'],
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'high': candle['high'],
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'low': candle['low'],
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'close': candle['close'],
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'volume': candle['volume']
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} for candle in env.data[-100:]]) # Use last 100 candles
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# Convert timestamp to datetime
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df_ohlcv['timestamp'] = pd.to_datetime(df_ohlcv['timestamp'], unit='ms')
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df_ohlcv.set_index('timestamp', inplace=True)
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# Extract buy/sell signals from trades
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buy_signals = []
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sell_signals = []
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if hasattr(env, 'trades') and env.trades:
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for trade in env.trades:
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if 'entry_time' in trade and 'entry' in trade:
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if trade['type'] == 'long':
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# Buy signal
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entry_time = pd.to_datetime(trade['entry_time'], unit='ms')
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buy_signals.append((entry_time, trade['entry']))
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# Sell signal if closed
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if 'exit_time' in trade and 'exit' in trade and trade['exit'] > 0:
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exit_time = pd.to_datetime(trade['exit_time'], unit='ms')
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sell_signals.append((exit_time, trade['exit']))
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elif trade['type'] == 'short':
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# Sell short signal
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entry_time = pd.to_datetime(trade['entry_time'], unit='ms')
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sell_signals.append((entry_time, trade['entry']))
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# Buy to cover signal if closed
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if 'exit_time' in trade and 'exit' in trade and trade['exit'] > 0:
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exit_time = pd.to_datetime(trade['exit_time'], unit='ms')
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buy_signals.append((exit_time, trade['exit']))
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# Log to TensorBoard
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log_ohlcv_to_tensorboard(
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agent.writer,
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df_ohlcv,
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buy_signals,
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sell_signals,
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episode,
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tag_prefix=f"episode_{episode}"
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)
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while not done:
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# Select action
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action = agent.select_action(state)
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@ -1920,6 +1969,72 @@ async def train_agent(agent, env, num_episodes=1000, max_steps_per_episode=1000,
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if episode % 10 == 0 or episode == num_episodes - 1:
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visualize_training_results(env, agent, episode)
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# After episode is complete, log final state with all trades
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if episode % 10 == 0 or episode == num_episodes - 1:
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# Create a DataFrame from the environment's data
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df_ohlcv = pd.DataFrame([{
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'timestamp': candle['timestamp'],
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'open': candle['open'],
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'high': candle['high'],
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'low': candle['low'],
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'close': candle['close'],
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'volume': candle['volume']
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} for candle in env.data[-100:]]) # Use last 100 candles
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# Convert timestamp to datetime
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df_ohlcv['timestamp'] = pd.to_datetime(df_ohlcv['timestamp'], unit='ms')
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df_ohlcv.set_index('timestamp', inplace=True)
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# Extract buy/sell signals from trades
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buy_signals = []
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sell_signals = []
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if hasattr(env, 'trades') and env.trades:
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for trade in env.trades:
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if 'entry_time' in trade and 'entry' in trade:
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if trade['type'] == 'long':
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# Buy signal
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entry_time = pd.to_datetime(trade['entry_time'], unit='ms')
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buy_signals.append((entry_time, trade['entry']))
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# Sell signal if closed
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if 'exit_time' in trade and 'exit' in trade and trade['exit'] > 0:
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exit_time = pd.to_datetime(trade['exit_time'], unit='ms')
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sell_signals.append((exit_time, trade['exit']))
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elif trade['type'] == 'short':
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# Sell short signal
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entry_time = pd.to_datetime(trade['entry_time'], unit='ms')
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sell_signals.append((entry_time, trade['entry']))
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# Buy to cover signal if closed
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if 'exit_time' in trade and 'exit' in trade and trade['exit'] > 0:
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exit_time = pd.to_datetime(trade['exit_time'], unit='ms')
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buy_signals.append((exit_time, trade['exit']))
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# Log to TensorBoard - use a fixed tag to overwrite previous charts
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log_ohlcv_to_tensorboard(
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agent.writer,
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df_ohlcv,
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buy_signals,
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sell_signals,
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episode,
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tag_prefix="latest_trading_data" # Use a fixed tag to overwrite previous charts
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)
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# Create visualization - only keep the latest one
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os.makedirs("visualizations", exist_ok=True)
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# Remove previous visualizations to save disk space
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for file in os.listdir("visualizations"):
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if file.startswith("training_episode_") and file.endswith(".png"):
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try:
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os.remove(os.path.join("visualizations", file))
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except:
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pass
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# Create new visualization
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visualize_training_results(env, agent, episode)
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except Exception as e:
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logger.error(f"Error in episode {episode}: {e}")
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logger.error(f"Traceback: {traceback.format_exc()}")
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@ -1945,8 +2060,8 @@ async def train_agent(agent, env, num_episodes=1000, max_steps_per_episode=1000,
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return stats
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def plot_training_results(stats):
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"""Plot detailed training results"""
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plt.figure(figsize=(20, 15))
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"""Plot training results"""
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plt.figure(figsize=(15, 15))
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# Plot rewards
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plt.subplot(3, 2, 1)
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@ -1957,10 +2072,10 @@ def plot_training_results(stats):
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# Plot balance
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plt.subplot(3, 2, 2)
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plt.plot(stats['episode_profits'])
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plt.title('Account Balance')
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plt.plot(stats['episode_profits']) # Changed from 'episode_pnls'
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plt.title('Episode Profits')
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plt.xlabel('Episode')
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plt.ylabel('Balance ($)')
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plt.ylabel('Profit ($)')
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# Plot win rate
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plt.subplot(3, 2, 3)
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@ -1969,35 +2084,24 @@ def plot_training_results(stats):
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plt.xlabel('Episode')
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plt.ylabel('Win Rate (%)')
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# Plot episode PnL
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# Plot trade count
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plt.subplot(3, 2, 4)
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plt.plot(stats['episode_pnls'])
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plt.title('Episode PnL')
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plt.plot(stats['trade_counts']) # Changed from 'episode_lengths'
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plt.title('Number of Trades')
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plt.xlabel('Episode')
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plt.ylabel('PnL ($)')
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plt.ylabel('Trades')
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# Plot cumulative PnL
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# Plot prediction accuracy
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plt.subplot(3, 2, 5)
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plt.plot(stats['cumulative_pnl'])
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plt.title('Cumulative PnL')
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plt.plot(stats['prediction_accuracies']) # Changed from 'prediction_accuracy'
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plt.title('Prediction Accuracy')
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plt.xlabel('Episode')
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plt.ylabel('Cumulative PnL ($)')
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# Plot drawdown
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plt.subplot(3, 2, 6)
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plt.plot(stats['drawdowns'])
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plt.title('Maximum Drawdown')
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plt.xlabel('Episode')
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plt.ylabel('Drawdown (%)')
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plt.ylabel('Accuracy (%)')
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# Save the figure
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plt.tight_layout()
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plt.savefig('training_results.png')
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# Save statistics to CSV
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df = pd.DataFrame(stats)
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df.to_csv('training_stats.csv', index=False)
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logger.info("Training statistics saved to training_stats.csv and training_results.png")
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plt.close()
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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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@ -2515,6 +2619,128 @@ def visualize_training_results(env, agent, episode_num):
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logger.error(f"Error creating visualization: {e}")
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logger.error(f"Traceback: {traceback.format_exc()}")
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def log_ohlcv_to_tensorboard(writer, df_ohlcv, buy_signals, sell_signals, step, tag_prefix="trading"):
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"""
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Log OHLCV chart with buy/sell signals to TensorBoard
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Parameters:
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-----------
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writer : torch.utils.tensorboard.SummaryWriter
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TensorBoard writer instance
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df_ohlcv : pandas.DataFrame
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DataFrame with OHLCV data
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buy_signals : list of tuples
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List of (datetime, price) tuples for buy signals
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sell_signals : list of tuples
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List of (datetime, price) tuples for sell signals
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step : int
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Global step value to record
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tag_prefix : str
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Prefix for the tag in TensorBoard
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"""
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try:
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from matplotlib.figure import Figure
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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import numpy as np
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# Check if DataFrame is empty
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if df_ohlcv.empty:
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logger.warning("Empty OHLCV DataFrame, skipping visualization")
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return
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# Create figure
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fig = Figure(figsize=(12, 8))
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canvas = FigureCanvas(fig)
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# Create subplots for price and volume
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ax1 = fig.add_subplot(2, 1, 1) # Price chart
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ax2 = fig.add_subplot(2, 1, 2, sharex=ax1) # Volume chart
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# Plot OHLC
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dates = mdates.date2num(df_ohlcv.index.to_pydatetime())
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ohlc = np.column_stack((dates, df_ohlcv['open'], df_ohlcv['high'], df_ohlcv['low'], df_ohlcv['close']))
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# Plot candlestick chart
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from matplotlib.lines import Line2D
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from matplotlib.patches import Rectangle
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width = 0.6 / (len(df_ohlcv) + 1) # Adjust width based on number of candles
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for i, (date, open_price, high, low, close) in enumerate(ohlc):
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# Determine candle color
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if close >= open_price:
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color = 'green'
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body_bottom = open_price
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body_height = close - open_price
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else:
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color = 'red'
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body_bottom = close
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body_height = open_price - close
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# Plot candle body
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rect = Rectangle(
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xy=(date - width/2, body_bottom),
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width=width,
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height=body_height,
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facecolor=color,
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edgecolor='black',
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alpha=0.8
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)
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ax1.add_patch(rect)
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# Plot wick
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ax1.plot([date, date], [low, high], color='black', linewidth=1)
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# Plot buy signals
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if buy_signals:
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buy_dates = mdates.date2num([x[0] for x in buy_signals])
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buy_prices = [x[1] for x in buy_signals]
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ax1.scatter(buy_dates, buy_prices, marker='^', color='green', s=100, label='Buy')
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# Plot sell signals
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if sell_signals:
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sell_dates = mdates.date2num([x[0] for x in sell_signals])
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sell_prices = [x[1] for x in sell_signals]
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ax1.scatter(sell_dates, sell_prices, marker='v', color='red', s=100, label='Sell')
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# Plot volume
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ax2.bar(dates, df_ohlcv['volume'], width=width, color='blue', alpha=0.5)
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# Format axes
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ax1.set_title(f'OHLC with Buy/Sell Signals - {tag_prefix}')
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ax1.set_ylabel('Price')
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ax1.legend()
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ax1.grid(True)
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ax2.set_xlabel('Date')
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ax2.set_ylabel('Volume')
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ax2.grid(True)
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# Format date
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date_format = mdates.DateFormatter('%Y-%m-%d %H:%M')
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ax2.xaxis.set_major_formatter(date_format)
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fig.autofmt_xdate()
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# Adjust layout
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fig.tight_layout()
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# Log to TensorBoard
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if tag_prefix == "latest_trading_data":
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# For the latest data, use a fixed tag without step to overwrite previous charts
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writer.add_figure(f"{tag_prefix}/ohlcv_chart", fig)
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else:
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# For other charts, include the step
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writer.add_figure(f"{tag_prefix}/ohlcv_chart", fig, global_step=step)
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# Clean up
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plt.close(fig)
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except Exception as e:
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logger.error(f"Error in log_ohlcv_to_tensorboard: {e}")
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logger.error(f"Traceback: {traceback.format_exc()}")
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if __name__ == "__main__":
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try:
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asyncio.run(main())
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