new cnn nn implementation (wip)
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Dobromir Popov
parent
114ced03b7
commit
50eb50696b
244
realtime.py
244
realtime.py
@ -19,6 +19,7 @@ import os
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from datetime import datetime, timedelta
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import pytz
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import tzlocal
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import threading
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# Configure logging with more detailed format
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logging.basicConfig(
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@ -31,6 +32,144 @@ logging.basicConfig(
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)
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logger = logging.getLogger(__name__)
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# Neural Network integration (conditional import)
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NN_ENABLED = os.environ.get('ENABLE_NN_MODELS', '0') == '1'
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nn_orchestrator = None
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nn_inference_thread = None
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if NN_ENABLED:
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try:
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import sys
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# Add project root to sys.path if needed
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project_root = os.path.dirname(os.path.abspath(__file__))
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if project_root not in sys.path:
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sys.path.append(project_root)
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from NN.main import NeuralNetworkOrchestrator
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logger.info("Neural Network module enabled")
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except ImportError as e:
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logger.warning(f"Failed to import Neural Network module, disabling NN features: {str(e)}")
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NN_ENABLED = False
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# NN utility functions
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def setup_neural_network():
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"""Initialize the neural network components if enabled"""
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global nn_orchestrator, NN_ENABLED
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if not NN_ENABLED:
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return False
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try:
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# Get configuration from environment variables or use defaults
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symbol = os.environ.get('NN_SYMBOL', 'BTC/USDT')
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timeframes = os.environ.get('NN_TIMEFRAMES', '1m,5m,1h,4h,1d').split(',')
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output_size = int(os.environ.get('NN_OUTPUT_SIZE', '3')) # 3 for BUY/HOLD/SELL
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# Configure the orchestrator
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config = {
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'symbol': symbol,
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'timeframes': timeframes,
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'window_size': int(os.environ.get('NN_WINDOW_SIZE', '20')),
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'n_features': 5, # OHLCV
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'output_size': output_size,
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'model_dir': 'NN/models/saved',
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'data_dir': 'NN/data'
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}
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# Initialize the orchestrator
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logger.info(f"Initializing Neural Network Orchestrator with config: {config}")
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nn_orchestrator = NeuralNetworkOrchestrator(config)
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# Start inference thread if enabled
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inference_interval = int(os.environ.get('NN_INFERENCE_INTERVAL', '60'))
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if inference_interval > 0:
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start_nn_inference_thread(inference_interval)
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return True
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except Exception as e:
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logger.error(f"Error setting up neural network: {str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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NN_ENABLED = False
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return False
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def start_nn_inference_thread(interval_seconds):
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"""Start a background thread to periodically run inference with the neural network"""
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global nn_inference_thread
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if not NN_ENABLED or nn_orchestrator is None:
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logger.warning("Cannot start inference thread - Neural Network not enabled or initialized")
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return False
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def inference_worker():
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"""Worker function for the inference thread"""
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model_type = os.environ.get('NN_MODEL_TYPE', 'cnn')
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timeframe = os.environ.get('NN_TIMEFRAME', '1h')
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logger.info(f"Starting neural network inference thread with {interval_seconds}s interval")
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logger.info(f"Using model type: {model_type}, timeframe: {timeframe}")
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# Wait a bit for charts to initialize
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time.sleep(5)
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# Track active charts
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active_charts = []
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while True:
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try:
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# Find active charts if we don't have them yet
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if not active_charts and 'charts' in globals():
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active_charts = globals()['charts']
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logger.info(f"Found {len(active_charts)} active charts for NN signals")
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# Run inference
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result = nn_orchestrator.run_inference_pipeline(
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model_type=model_type,
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timeframe=timeframe
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)
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if result:
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# Log the result
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logger.info(f"Neural network inference result: {result}")
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# Add signal to charts
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if active_charts:
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try:
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if 'action' in result:
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action = result['action']
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timestamp = datetime.fromisoformat(result['timestamp'].replace('Z', '+00:00'))
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# Get probability if available
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probability = None
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if 'probability' in result:
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probability = result['probability']
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elif 'probabilities' in result:
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probability = result['probabilities'].get(action, None)
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# Add signal to each chart
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for chart in active_charts:
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if hasattr(chart, 'add_nn_signal'):
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chart.add_nn_signal(action, timestamp, probability)
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except Exception as e:
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logger.error(f"Error adding NN signal to chart: {str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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# Sleep for the interval
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time.sleep(interval_seconds)
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except Exception as e:
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logger.error(f"Error in inference thread: {str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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time.sleep(5) # Wait a bit before retrying
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# Create and start the thread
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nn_inference_thread = threading.Thread(target=inference_worker, daemon=True)
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nn_inference_thread.start()
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return True
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# Try to get local timezone, default to Sofia/EET if not available
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try:
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local_timezone = tzlocal.get_localzone()
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@ -1125,7 +1264,10 @@ class CandleCache:
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return pd.DataFrame()
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class RealTimeChart:
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"""Real-time chart using Dash and Plotly with WebSocket data feed"""
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def __init__(self, symbol: str):
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"""Initialize the chart with necessary components"""
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self.symbol = symbol
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# Create a multi-page Dash app instead of a simple Dash app
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self.app = dash.Dash(__name__,
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@ -1143,8 +1285,12 @@ class RealTimeChart:
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self.historical_data = BinanceHistoricalData() # For fetching historical data
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self.last_cache_save_time = time.time() # Track last time we saved cache to disk
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self.first_render = True # Flag to track first render
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# Storage for NN signals
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self.nn_signals = []
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logger.info(f"Initializing RealTimeChart for {symbol}")
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# Load historical data for longer timeframes at startup
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self._load_historical_data()
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@ -1791,6 +1937,57 @@ class RealTimeChart:
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fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
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fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
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# Add neural network signals as annotations if available
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if hasattr(self, 'nn_signals') and self.nn_signals:
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for signal in self.nn_signals:
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try:
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# Skip HOLD signals for clarity
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if signal['type'] == 'HOLD':
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continue
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# Check if this signal's timestamp is within the visible range
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signal_ts = signal['timestamp']
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# Only add annotations for signals in the visible time range
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if df is not None and not df.empty:
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if signal_ts < df.index.min() or signal_ts > df.index.max():
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continue
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# Set color and symbol based on signal type
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if signal['type'] == 'BUY':
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color = 'green'
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symbol = '▲' # Up triangle
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y_position = df['low'].min() * 0.98 # Below the candles
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else: # SELL
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color = 'red'
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symbol = '▼' # Down triangle
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y_position = df['high'].max() * 1.02 # Above the candles
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# Add probability if available
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text = f"{signal['type']}"
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if signal['probability'] is not None:
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text += f" ({signal['probability']:.2f})"
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# Add annotation
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fig.add_annotation(
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x=signal_ts,
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y=y_position,
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text=text,
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showarrow=True,
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arrowhead=2,
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arrowsize=1,
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arrowwidth=2,
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arrowcolor=color,
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font=dict(size=12, color=color),
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bgcolor='rgba(255, 255, 255, 0.8)',
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bordercolor=color,
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borderwidth=1,
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borderpad=4,
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row=1, col=1
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)
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except Exception as e:
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logger.error(f"Error adding NN signal annotation: {str(e)}")
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return fig
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except Exception as layout_error:
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logger.error(f"Error updating layout: {str(layout_error)}")
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@ -2573,11 +2770,56 @@ class RealTimeChart:
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import traceback
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logger.error(traceback.format_exc())
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def add_nn_signal(self, signal_type, timestamp, probability=None):
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"""Add a neural network signal to be displayed on the chart
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Args:
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signal_type: The type of signal (BUY, SELL, HOLD)
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timestamp: The timestamp for the signal
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probability: Optional probability/confidence value
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"""
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if signal_type not in ['BUY', 'SELL', 'HOLD']:
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logger.warning(f"Invalid NN signal type: {signal_type}")
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return
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# Convert timestamp to datetime if it's not already
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if not isinstance(timestamp, datetime):
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try:
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if isinstance(timestamp, str):
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timestamp = datetime.fromisoformat(timestamp.replace('Z', '+00:00'))
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elif isinstance(timestamp, (int, float)):
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timestamp = datetime.fromtimestamp(timestamp / 1000.0)
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except Exception as e:
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logger.error(f"Error converting timestamp for NN signal: {str(e)}")
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timestamp = datetime.now()
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# Add the signal to our list
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self.nn_signals.append({
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'type': signal_type,
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'timestamp': timestamp,
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'probability': probability,
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'added': datetime.now()
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})
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# Only keep the most recent 50 signals
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if len(self.nn_signals) > 50:
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self.nn_signals = self.nn_signals[-50:]
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logger.info(f"Added NN signal: {signal_type} at {timestamp}")
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async def main():
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global charts # Make charts globally accessible for NN integration
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symbols = ["ETH/USDT", "BTC/USDT"]
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logger.info(f"Starting application for symbols: {symbols}")
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# Initialize neural network if enabled
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if NN_ENABLED:
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logger.info("Initializing Neural Network integration...")
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if setup_neural_network():
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logger.info("Neural Network integration initialized successfully")
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else:
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logger.warning("Neural Network integration failed to initialize")
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charts = []
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websocket_tasks = []
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