LR module possibly working
This commit is contained in:
Dobromir Popov
@ -60,6 +60,10 @@ except ImportError:
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'models': {}
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}
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def get_models_by_type(self, model_type: str):
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"""Get models by type - fallback implementation returns empty dict"""
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return {}
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def register_model(self, model, weight=1.0):
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return True
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@ -305,7 +309,8 @@ class TradingDashboard:
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], className="row g-2 mb-3"),
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# Bottom row - Session performance and system status
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html.Div([
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html.Div([
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# Session performance - 1/3 width
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html.Div([
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html.Div([
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@ -313,10 +318,16 @@ class TradingDashboard:
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html.I(className="fas fa-chart-pie me-2"),
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"Session Performance"
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], className="card-title mb-2"),
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html.Button(
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"Clear Session",
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id="clear-history-btn",
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className="btn btn-sm btn-outline-danger mb-2",
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n_clicks=0
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),
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html.Div(id="session-performance")
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], className="card-body p-2")
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], className="card", style={"width": "32%"}),
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# Closed Trades History - 1/3 width
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html.Div([
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html.Div([
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@ -325,12 +336,6 @@ class TradingDashboard:
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"Closed Trades History"
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], className="card-title mb-2"),
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html.Div([
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html.Button(
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"Clear History",
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id="clear-history-btn",
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className="btn btn-sm btn-outline-danger mb-2",
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n_clicks=0
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),
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html.Div(
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id="closed-trades-table",
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style={"height": "300px", "overflowY": "auto"}
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@ -34,6 +34,7 @@ from core.config import get_config
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from core.data_provider import DataProvider, MarketTick
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator, TradingAction
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from core.trading_executor import TradingExecutor, Position, TradeRecord
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from core.unified_data_stream import UnifiedDataStream, TrainingDataPacket, UIDataPacket
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logger = logging.getLogger(__name__)
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@ -242,339 +243,220 @@ class RealTimeScalpingDashboard:
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"""Real-time scalping dashboard with WebSocket streaming and ultra-low latency"""
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def __init__(self, data_provider: DataProvider = None, orchestrator: EnhancedTradingOrchestrator = None, trading_executor: TradingExecutor = None):
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"""Initialize the real-time dashboard with WebSocket streaming and MEXC integration"""
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"""Initialize the real-time scalping dashboard with unified data stream"""
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self.config = get_config()
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self.data_provider = data_provider or DataProvider()
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self.orchestrator = orchestrator or EnhancedTradingOrchestrator(self.data_provider)
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self.trading_executor = trading_executor or TradingExecutor()
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self.orchestrator = orchestrator
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self.trading_executor = trading_executor
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# Verify universal data format compliance
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logger.info("UNIVERSAL DATA FORMAT VERIFICATION:")
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logger.info("Required 5 timeseries streams:")
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logger.info(" 1. ETH/USDT ticks (1s)")
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logger.info(" 2. ETH/USDT 1m")
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logger.info(" 3. ETH/USDT 1h")
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logger.info(" 4. ETH/USDT 1d")
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logger.info(" 5. BTC/USDT ticks (reference)")
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# Preload 300s of data for better initial performance
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logger.info("PRELOADING 300s OF DATA FOR INITIAL PERFORMANCE:")
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preload_results = self.data_provider.preload_all_symbols_data(['1s', '1m', '5m', '15m', '1h', '1d'])
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# Log preload results
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for symbol, timeframe_results in preload_results.items():
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for timeframe, success in timeframe_results.items():
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status = "OK" if success else "FAIL"
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logger.info(f" {status} {symbol} {timeframe}")
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# Test universal data adapter
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try:
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universal_stream = self.orchestrator.universal_adapter.get_universal_data_stream()
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if universal_stream:
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is_valid, issues = self.orchestrator.universal_adapter.validate_universal_format(universal_stream)
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if is_valid:
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logger.info("Universal data format validation PASSED")
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logger.info(f" ETH ticks: {len(universal_stream.eth_ticks)} samples")
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logger.info(f" ETH 1m: {len(universal_stream.eth_1m)} candles")
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logger.info(f" ETH 1h: {len(universal_stream.eth_1h)} candles")
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logger.info(f" ETH 1d: {len(universal_stream.eth_1d)} candles")
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logger.info(f" BTC reference: {len(universal_stream.btc_ticks)} samples")
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logger.info(f" Data quality: {universal_stream.metadata['data_quality']['overall_score']:.2f}")
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else:
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logger.warning(f"FAIL: Universal data format validation FAILED: {issues}")
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else:
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logger.warning("FAIL: Failed to get universal data stream")
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except Exception as e:
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logger.error(f"FAIL: Universal data format test failed: {e}")
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# Initialize new trading session with MEXC integration
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self.trading_session = TradingSession(trading_executor=self.trading_executor)
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# Timezone setup
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# Initialize timezone (Sofia timezone)
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import pytz
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self.timezone = pytz.timezone('Europe/Sofia')
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# Dashboard state - now using session-based metrics
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self.recent_decisions = []
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# Initialize unified data stream for centralized data distribution
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self.unified_stream = UnifiedDataStream(self.data_provider, self.orchestrator)
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# Real-time price streaming data
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self.live_prices = {
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'ETH/USDT': 0.0,
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'BTC/USDT': 0.0
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}
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# Register dashboard as data consumer
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self.stream_consumer_id = self.unified_stream.register_consumer(
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consumer_name="ScalpingDashboard",
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callback=self._handle_unified_stream_data,
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data_types=['ui_data', 'training_data', 'ticks', 'ohlcv']
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)
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# Real-time tick buffer for main chart (WebSocket direct feed)
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self.live_tick_buffer = {
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'ETH/USDT': [],
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'BTC/USDT': []
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}
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self.max_tick_buffer_size = 200 # Keep last 200 ticks for main chart
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# Real-time chart data (no caching - always fresh)
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# This matches our universal format: ETH (1s, 1m, 1h, 1d) + BTC (1s)
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self.chart_data = {
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'ETH/USDT': {
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'1s': pd.DataFrame(), # ETH ticks/1s data
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'1m': pd.DataFrame(), # ETH 1m data
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'1h': pd.DataFrame(), # ETH 1h data
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'1d': pd.DataFrame() # ETH 1d data
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},
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'BTC/USDT': {
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'1s': pd.DataFrame() # BTC reference ticks
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}
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}
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# Training data structures (like the old dashboard)
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self.tick_cache = deque(maxlen=900) # 15 minutes of ticks at 1 tick/second
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self.one_second_bars = deque(maxlen=800) # 800 seconds of 1s bars
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# Dashboard data storage (updated from unified stream)
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self.tick_cache = deque(maxlen=2500)
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self.one_second_bars = deque(maxlen=900)
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self.current_prices = {}
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self.is_streaming = False
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self.training_data_available = False
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# WebSocket streaming control - now using DataProvider centralized distribution
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# Enhanced training integration
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self.latest_training_data: Optional[TrainingDataPacket] = None
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self.latest_ui_data: Optional[UIDataPacket] = None
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# Trading session with MEXC integration
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self.trading_session = TradingSession(trading_executor=trading_executor)
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# Dashboard state
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self.streaming = False
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self.data_provider_subscriber_id = None
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self.data_lock = Lock()
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self.app = dash.Dash(__name__, external_stylesheets=[dbc.themes.CYBORG])
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# Dynamic throttling control - more aggressive optimization
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self.update_frequency = 5000 # Start with 2 seconds (2000ms) - more conservative
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self.min_frequency = 500 # Maximum 5 seconds when heavily throttled
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self.max_frequency = 10000 # Minimum 1 second when optimal
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self.last_callback_time = 0
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self.callback_duration_history = []
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self.throttle_level = 0 # 0 = no throttle, 1-3 = increasing throttle levels (reduced from 5)
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# Initialize missing attributes for callback functionality
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self.data_lock = Lock()
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self.live_prices = {'ETH/USDT': 0.0, 'BTC/USDT': 0.0}
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self.chart_data = {
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'ETH/USDT': {'1s': pd.DataFrame(), '1m': pd.DataFrame(), '1h': pd.DataFrame(), '1d': pd.DataFrame()},
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'BTC/USDT': {'1s': pd.DataFrame()}
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}
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self.recent_decisions = deque(maxlen=50)
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self.live_tick_buffer = {
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'ETH/USDT': deque(maxlen=1000),
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'BTC/USDT': deque(maxlen=1000)
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}
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self.max_tick_buffer_size = 1000
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# Performance tracking
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self.callback_performance = {
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'total_calls': 0,
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'successful_calls': 0,
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'avg_duration': 0.0,
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'last_update': datetime.now(),
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'throttle_active': False,
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'throttle_count': 0
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}
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# Throttling configuration
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self.throttle_threshold = 50 # Max callbacks per minute
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self.throttle_window = 60 # 1 minute window
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self.callback_times = deque(maxlen=self.throttle_threshold)
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# Initialize throttling attributes
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self.throttle_level = 0
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self.update_frequency = 2000 # Start with 2 seconds
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self.max_frequency = 1000 # Fastest update (1 second)
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self.min_frequency = 10000 # Slowest update (10 seconds)
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self.consecutive_fast_updates = 0
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self.consecutive_slow_updates = 0
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self.callback_duration_history = []
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self.last_callback_time = time.time()
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self.last_known_state = None
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# Create Dash app with real-time updates
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self.app = dash.Dash(__name__,
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external_stylesheets=['https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css'])
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# WebSocket threads tracking
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self.websocket_threads = []
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# Inject JavaScript for debugging client-side data loading
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self.app.index_string = '''
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<!DOCTYPE html>
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<html>
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<head>
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{%metas%}
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<title>{%title%}</title>
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{%favicon%}
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{%css%}
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<script>
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// Debug logging for Dash callbacks
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window.dashDebug = {
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callbackCount: 0,
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lastUpdate: null,
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errors: [],
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log: function(message, data) {
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const timestamp = new Date().toISOString();
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console.log(`[DASH DEBUG ${timestamp}] ${message}`, data || '');
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// Store in window for inspection
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if (!window.dashLogs) window.dashLogs = [];
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window.dashLogs.push({timestamp, message, data});
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// Keep only last 100 logs
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if (window.dashLogs.length > 100) {
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window.dashLogs = window.dashLogs.slice(-100);
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}
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},
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logCallback: function(callbackId, inputs, outputs) {
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this.callbackCount++;
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this.lastUpdate = new Date();
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this.log(`Callback #${this.callbackCount} - ID: ${callbackId}`, {
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inputs: inputs,
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outputs: outputs,
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timestamp: this.lastUpdate
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});
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},
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logError: function(error) {
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this.errors.push({
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timestamp: new Date(),
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error: error.toString(),
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stack: error.stack
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});
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this.log('ERROR', error);
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}
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};
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// Override fetch to monitor _dash-update-component requests
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const originalFetch = window.fetch;
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window.fetch = function(...args) {
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const url = args[0];
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const options = args[1] || {};
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if (typeof url === 'string' && url.includes('_dash-update-component')) {
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window.dashDebug.log('FETCH REQUEST to _dash-update-component', {
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url: url,
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method: options.method || 'GET',
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body: options.body ? JSON.parse(options.body) : null
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});
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return originalFetch.apply(this, args)
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.then(response => {
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window.dashDebug.log('FETCH RESPONSE from _dash-update-component', {
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status: response.status,
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statusText: response.statusText,
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ok: response.ok
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});
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// Clone response to read body without consuming it
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const clonedResponse = response.clone();
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clonedResponse.json().then(data => {
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window.dashDebug.log('RESPONSE DATA from _dash-update-component', data);
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}).catch(err => {
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window.dashDebug.log('ERROR parsing response JSON', err);
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});
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return response;
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})
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.catch(error => {
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window.dashDebug.logError(error);
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throw error;
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});
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}
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return originalFetch.apply(this, args);
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};
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// Monitor DOM changes for component updates
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document.addEventListener('DOMContentLoaded', function() {
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window.dashDebug.log('DOM LOADED - Starting dashboard monitoring');
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// Monitor specific elements for changes
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const elementsToWatch = [
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'current-balance',
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'session-duration',
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'eth-price',
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'main-eth-1s-chart',
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'actions-log'
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||||
];
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elementsToWatch.forEach(elementId => {
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const element = document.getElementById(elementId);
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if (element) {
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const observer = new MutationObserver(function(mutations) {
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mutations.forEach(function(mutation) {
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if (mutation.type === 'childList' || mutation.type === 'attributes') {
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window.dashDebug.log(`ELEMENT UPDATED: ${elementId}`, {
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||||
type: mutation.type,
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target: mutation.target.tagName,
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||||
newValue: element.textContent || element.innerHTML.substring(0, 100)
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||||
});
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||||
}
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||||
});
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||||
});
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||||
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||||
observer.observe(element, {
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||||
childList: true,
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||||
subtree: true,
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||||
attributes: true,
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||||
attributeOldValue: true
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||||
});
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||||
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window.dashDebug.log(`WATCHING ELEMENT: ${elementId}`);
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||||
} else {
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window.dashDebug.log(`ELEMENT NOT FOUND: ${elementId}`);
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||||
}
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||||
});
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||||
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||||
// Check for Dash app initialization
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||||
const checkDashApp = setInterval(() => {
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||||
if (window.dash_clientside) {
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||||
window.dashDebug.log('DASH CLIENTSIDE AVAILABLE');
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||||
clearInterval(checkDashApp);
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||||
}
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||||
if (window._dash_renderer) {
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||||
window.dashDebug.log('DASH RENDERER AVAILABLE');
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||||
clearInterval(checkDashApp);
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||||
}
|
||||
}, 100);
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||||
|
||||
// Log interval component status
|
||||
setInterval(() => {
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||||
const intervalElement = document.querySelector('[data-dash-is-loading="true"]');
|
||||
if (intervalElement) {
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||||
window.dashDebug.log('DASH COMPONENT LOADING', intervalElement.id);
|
||||
}
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||||
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||||
// Log current callback status
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||||
window.dashDebug.log('STATUS CHECK', {
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||||
callbackCount: window.dashDebug.callbackCount,
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||||
lastUpdate: window.dashDebug.lastUpdate,
|
||||
errorCount: window.dashDebug.errors.length,
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||||
dashRenderer: !!window._dash_renderer,
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||||
dashClientside: !!window.dash_clientside
|
||||
});
|
||||
}, 5000); // Every 5 seconds
|
||||
});
|
||||
|
||||
// Helper function to get debug info
|
||||
window.getDashDebugInfo = function() {
|
||||
return {
|
||||
callbackCount: window.dashDebug.callbackCount,
|
||||
lastUpdate: window.dashDebug.lastUpdate,
|
||||
errors: window.dashDebug.errors,
|
||||
logs: window.dashLogs || [],
|
||||
dashRenderer: !!window._dash_renderer,
|
||||
dashClientside: !!window.dash_clientside
|
||||
};
|
||||
};
|
||||
|
||||
// Helper function to clear logs
|
||||
window.clearDashLogs = function() {
|
||||
window.dashLogs = [];
|
||||
window.dashDebug.errors = [];
|
||||
window.dashDebug.callbackCount = 0;
|
||||
console.log('Dash debug logs cleared');
|
||||
};
|
||||
</script>
|
||||
</head>
|
||||
<body>
|
||||
{%app_entry%}
|
||||
<footer>
|
||||
{%config%}
|
||||
{%scripts%}
|
||||
{%renderer%}
|
||||
</footer>
|
||||
<script>
|
||||
// Additional debugging after Dash loads
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
setTimeout(() => {
|
||||
window.dashDebug.log('DASH APP FULLY LOADED');
|
||||
|
||||
// Try to access Dash internals
|
||||
if (window._dash_renderer && window._dash_renderer._store) {
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||||
window.dashDebug.log('DASH STORE AVAILABLE', Object.keys(window._dash_renderer._store.getState()));
|
||||
}
|
||||
}, 2000);
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
'''
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||||
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||||
# Setup layout and callbacks
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||||
# Setup dashboard
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||||
self._setup_layout()
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||||
self._setup_callbacks()
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||||
self._start_real_time_streaming()
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||||
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||||
# Initial data fetch to populate charts immediately
|
||||
logger.info("Fetching initial data for all charts...")
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||||
self._refresh_live_data()
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||||
# Start streaming automatically
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||||
self._initialize_streaming()
|
||||
|
||||
# Start orchestrator trading thread
|
||||
logger.info("Starting AI orchestrator trading thread...")
|
||||
self._start_orchestrator_trading()
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||||
logger.info("Real-Time Scalping Dashboard initialized with unified data stream")
|
||||
logger.info(f"Stream consumer ID: {self.stream_consumer_id}")
|
||||
logger.info(f"Enhanced RL training integration: {'ENABLED' if orchestrator else 'DISABLED'}")
|
||||
logger.info(f"MEXC trading: {'ENABLED' if trading_executor and trading_executor.trading_enabled else 'DISABLED'}")
|
||||
|
||||
def _initialize_streaming(self):
|
||||
"""Initialize streaming and populate initial data"""
|
||||
try:
|
||||
logger.info("Initializing dashboard streaming and data...")
|
||||
|
||||
# Start unified data streaming
|
||||
self._start_real_time_streaming()
|
||||
|
||||
# Initialize chart data with some basic data
|
||||
self._initialize_chart_data()
|
||||
|
||||
# Start background data refresh
|
||||
self._start_background_data_refresh()
|
||||
|
||||
logger.info("Dashboard streaming initialized successfully")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error initializing streaming: {e}")
|
||||
|
||||
def _initialize_chart_data(self):
|
||||
"""Initialize chart data with basic data to prevent empty charts"""
|
||||
try:
|
||||
logger.info("Initializing chart data...")
|
||||
|
||||
# Get initial data for charts
|
||||
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
||||
try:
|
||||
# Get current price
|
||||
current_price = self.data_provider.get_current_price(symbol)
|
||||
if current_price and current_price > 0:
|
||||
self.live_prices[symbol] = current_price
|
||||
logger.info(f"Initial price for {symbol}: ${current_price:.2f}")
|
||||
|
||||
# Create initial tick data
|
||||
initial_tick = {
|
||||
'timestamp': datetime.now(),
|
||||
'price': current_price,
|
||||
'volume': 0.0,
|
||||
'quantity': 0.0,
|
||||
'side': 'buy',
|
||||
'open': current_price,
|
||||
'high': current_price,
|
||||
'low': current_price,
|
||||
'close': current_price
|
||||
}
|
||||
self.live_tick_buffer[symbol].append(initial_tick)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error getting initial price for {symbol}: {e}")
|
||||
# Set default price
|
||||
default_price = 3500.0 if 'ETH' in symbol else 70000.0
|
||||
self.live_prices[symbol] = default_price
|
||||
|
||||
# Get initial historical data for charts
|
||||
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
||||
timeframes = ['1s', '1m', '1h', '1d'] if symbol == 'ETH/USDT' else ['1s']
|
||||
|
||||
for timeframe in timeframes:
|
||||
try:
|
||||
# Get historical data
|
||||
data = self.data_provider.get_historical_data(symbol, timeframe, limit=100)
|
||||
if data is not None and not data.empty:
|
||||
self.chart_data[symbol][timeframe] = data
|
||||
logger.info(f"Loaded {len(data)} candles for {symbol} {timeframe}")
|
||||
else:
|
||||
# Create empty DataFrame with proper structure
|
||||
self.chart_data[symbol][timeframe] = pd.DataFrame(columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
|
||||
logger.warning(f"No data available for {symbol} {timeframe}")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error loading data for {symbol} {timeframe}: {e}")
|
||||
self.chart_data[symbol][timeframe] = pd.DataFrame(columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
|
||||
|
||||
logger.info("Chart data initialization completed")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error initializing chart data: {e}")
|
||||
|
||||
def _start_background_data_refresh(self):
|
||||
"""Start background data refresh thread"""
|
||||
def background_refresh():
|
||||
logger.info("Background data refresh thread started")
|
||||
|
||||
while True:
|
||||
try:
|
||||
# Refresh live prices
|
||||
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
||||
try:
|
||||
current_price = self.data_provider.get_current_price(symbol)
|
||||
if current_price and current_price > 0:
|
||||
with self.data_lock:
|
||||
self.live_prices[symbol] = current_price
|
||||
|
||||
# Add to tick buffer
|
||||
tick_data = {
|
||||
'timestamp': datetime.now(),
|
||||
'price': current_price,
|
||||
'volume': 0.0,
|
||||
'quantity': 0.0,
|
||||
'side': 'buy',
|
||||
'open': current_price,
|
||||
'high': current_price,
|
||||
'low': current_price,
|
||||
'close': current_price
|
||||
}
|
||||
self.live_tick_buffer[symbol].append(tick_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error refreshing price for {symbol}: {e}")
|
||||
|
||||
# Sleep for 5 seconds
|
||||
time.sleep(5)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in background refresh: {e}")
|
||||
time.sleep(10)
|
||||
|
||||
# Start training data collection and model training
|
||||
logger.info("Starting model training and data collection...")
|
||||
self._start_training_data_collection()
|
||||
|
||||
logger.info("Real-Time Scalping Dashboard initialized with LIVE STREAMING")
|
||||
logger.info("WebSocket price streaming enabled")
|
||||
logger.info(f"Timezone: {self.timezone}")
|
||||
logger.info(f"Session Balance: ${self.trading_session.starting_balance:.2f}")
|
||||
logger.info("300s data preloading completed for faster initial performance")
|
||||
# Start background thread
|
||||
refresh_thread = Thread(target=background_refresh, daemon=True)
|
||||
refresh_thread.start()
|
||||
logger.info("Background data refresh thread started")
|
||||
|
||||
def _setup_layout(self):
|
||||
"""Setup the ultra-fast real-time dashboard layout"""
|
||||
@ -845,6 +727,7 @@ class RealTimeScalpingDashboard:
|
||||
open_positions = html.P("No open positions", className="text-muted")
|
||||
|
||||
pnl = f"${dashboard_instance.trading_session.total_pnl:+.2f}"
|
||||
total_fees = f"${dashboard_instance.trading_session.total_fees:.2f}"
|
||||
win_rate = f"{dashboard_instance.trading_session.get_win_rate()*100:.1f}%"
|
||||
total_trades = str(dashboard_instance.trading_session.total_trades)
|
||||
last_action = dashboard_instance.trading_session.last_action or "WAITING"
|
||||
@ -926,7 +809,7 @@ class RealTimeScalpingDashboard:
|
||||
|
||||
# Store last known state for throttling
|
||||
result = (
|
||||
current_balance, account_details, duration_str, open_positions, pnl, win_rate, total_trades, last_action, eth_price, btc_price, mexc_status,
|
||||
current_balance, account_details, duration_str, open_positions, pnl, total_fees, win_rate, total_trades, last_action, eth_price, btc_price, mexc_status,
|
||||
main_eth_chart, eth_1m_chart, eth_1h_chart, eth_1d_chart, btc_1s_chart,
|
||||
model_training_status, orchestrator_status, training_events_log, actions_log, debug_status
|
||||
)
|
||||
@ -962,64 +845,13 @@ class RealTimeScalpingDashboard:
|
||||
])
|
||||
|
||||
error_result = (
|
||||
"$100.00", "Change: $0.00 (0.0%)", "00:00:00", "0", "$0.00", "0%", "0", "ERROR", "Loading...", "Loading...", "OFFLINE",
|
||||
"$100.00", "Change: $0.00 (0.0%)", "00:00:00", "0", "$0.00", "$0.00", "0%", "0", "INIT", "Loading...", "Loading...", "OFFLINE",
|
||||
empty_fig, empty_fig, empty_fig, empty_fig, empty_fig,
|
||||
"Loading model status...", "Loading orchestrator status...", "Loading training events...",
|
||||
"Loading real-time data...", error_debug
|
||||
"Initializing models...", "Starting orchestrator...", "Loading events...",
|
||||
"Waiting for data...", error_debug
|
||||
)
|
||||
|
||||
# Store error state as last known state
|
||||
dashboard_instance.last_known_state = error_result
|
||||
return error_result
|
||||
|
||||
def _should_update_now(self, n_intervals):
|
||||
"""Determine if we should update based on dynamic throttling"""
|
||||
current_time = time.time()
|
||||
|
||||
# Always update the first few times
|
||||
if n_intervals <= 3:
|
||||
return True, "Initial updates"
|
||||
|
||||
# Check minimum time between updates
|
||||
time_since_last = (current_time - self.last_callback_time) * 1000 # Convert to ms
|
||||
expected_interval = self.update_frequency
|
||||
|
||||
# If we're being called too frequently, throttle
|
||||
if time_since_last < expected_interval * 0.8: # 80% of expected interval
|
||||
return False, f"Too frequent (last: {time_since_last:.0f}ms, expected: {expected_interval}ms)"
|
||||
|
||||
# If system is under load (based on throttle level), skip some updates
|
||||
if self.throttle_level > 3: # Only start skipping at level 4+ (more lenient)
|
||||
# Skip every 2nd, 3rd update etc. based on throttle level
|
||||
skip_factor = min(self.throttle_level - 2, 2) # Max skip factor of 2
|
||||
if n_intervals % skip_factor != 0:
|
||||
return False, f"Throttled (level {self.throttle_level}, skip factor {skip_factor})"
|
||||
|
||||
return True, "Normal update"
|
||||
|
||||
def _get_last_known_state(self):
|
||||
"""Return last known state for throttled updates"""
|
||||
if self.last_known_state is not None:
|
||||
return self.last_known_state
|
||||
|
||||
# Return minimal safe state if no previous state
|
||||
empty_fig = {
|
||||
'data': [],
|
||||
'layout': {
|
||||
'template': 'plotly_dark',
|
||||
'title': 'Initializing...',
|
||||
'paper_bgcolor': '#1e1e1e',
|
||||
'plot_bgcolor': '#1e1e1e'
|
||||
}
|
||||
}
|
||||
|
||||
return (
|
||||
"$100.00", "Change: $0.00 (0.0%)", "00:00:00", "0", "$0.00", "0%", "0", "INIT", "Loading...", "Loading...", "OFFLINE",
|
||||
empty_fig, empty_fig, empty_fig, empty_fig, empty_fig,
|
||||
"Initializing models...", "Starting orchestrator...", "Loading events...",
|
||||
"Waiting for data...", html.P("Initializing dashboard...", className="text-info")
|
||||
)
|
||||
|
||||
def _track_callback_performance(self, duration, success=True):
|
||||
"""Track callback performance and adjust throttling dynamically"""
|
||||
self.last_callback_time = time.time()
|
||||
@ -1077,6 +909,51 @@ class RealTimeScalpingDashboard:
|
||||
if len(self.callback_duration_history) % 10 == 0:
|
||||
logger.info(f"PERFORMANCE SUMMARY: Avg: {avg_duration:.2f}s, Throttle: {self.throttle_level}, Frequency: {self.update_frequency}ms")
|
||||
|
||||
def _should_update_now(self, n_intervals):
|
||||
"""Check if dashboard should update now based on throttling"""
|
||||
current_time = time.time()
|
||||
|
||||
# Always allow first few updates
|
||||
if n_intervals <= 3:
|
||||
return True, "Initial updates"
|
||||
|
||||
# Check if enough time has passed based on update frequency
|
||||
time_since_last = (current_time - self.last_callback_time) * 1000 # Convert to ms
|
||||
if time_since_last < self.update_frequency:
|
||||
return False, f"Throttled: {time_since_last:.0f}ms < {self.update_frequency}ms"
|
||||
|
||||
# Check throttle level
|
||||
if self.throttle_level > 0:
|
||||
# Skip some updates based on throttle level
|
||||
if n_intervals % (self.throttle_level + 1) != 0:
|
||||
return False, f"Throttle level {self.throttle_level}: skipping interval {n_intervals}"
|
||||
|
||||
return True, "Update allowed"
|
||||
|
||||
def _get_last_known_state(self):
|
||||
"""Get last known state for throttled updates"""
|
||||
if self.last_known_state:
|
||||
return self.last_known_state
|
||||
|
||||
# Return safe default state
|
||||
empty_fig = {
|
||||
'data': [],
|
||||
'layout': {
|
||||
'template': 'plotly_dark',
|
||||
'title': 'Loading...',
|
||||
'paper_bgcolor': '#1e1e1e',
|
||||
'plot_bgcolor': '#1e1e1e'
|
||||
}
|
||||
}
|
||||
|
||||
return (
|
||||
"$100.00", "Change: $0.00 (0.0%)", "00:00:00", "No positions", "$0.00", "$0.00", "0.0%", "0", "WAITING",
|
||||
"Loading...", "Loading...", "OFFLINE",
|
||||
empty_fig, empty_fig, empty_fig, empty_fig, empty_fig,
|
||||
"Initializing...", "Starting...", "Loading...", "Waiting...",
|
||||
html.P("Initializing dashboard...", className="text-info")
|
||||
)
|
||||
|
||||
def _reset_throttling(self):
|
||||
"""Reset throttling to optimal settings"""
|
||||
self.throttle_level = 0
|
||||
@ -1087,43 +964,33 @@ class RealTimeScalpingDashboard:
|
||||
logger.info(f"THROTTLING RESET: Level=0, Frequency={self.update_frequency}ms")
|
||||
|
||||
def _start_real_time_streaming(self):
|
||||
"""Start real-time data streaming using DataProvider centralized distribution"""
|
||||
logger.info("Starting real-time data streaming via DataProvider...")
|
||||
"""Start real-time streaming using unified data stream"""
|
||||
def start_streaming():
|
||||
try:
|
||||
logger.info("Starting unified data stream for dashboard")
|
||||
|
||||
# Start unified data streaming
|
||||
asyncio.run(self.unified_stream.start_streaming())
|
||||
|
||||
# Start orchestrator trading if available
|
||||
if self.orchestrator:
|
||||
self._start_orchestrator_trading()
|
||||
|
||||
# Start enhanced training data collection
|
||||
self._start_training_data_collection()
|
||||
|
||||
logger.info("Unified data streaming started successfully")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error starting unified data streaming: {e}")
|
||||
|
||||
# Start streaming in background thread
|
||||
streaming_thread = Thread(target=start_streaming, daemon=True)
|
||||
streaming_thread.start()
|
||||
|
||||
# Set streaming flag
|
||||
self.streaming = True
|
||||
|
||||
# Start DataProvider real-time streaming
|
||||
try:
|
||||
# Start the DataProvider's WebSocket streaming
|
||||
import asyncio
|
||||
def start_streaming():
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
loop.run_until_complete(self.data_provider.start_real_time_streaming())
|
||||
|
||||
streaming_thread = Thread(target=start_streaming, daemon=True)
|
||||
streaming_thread.start()
|
||||
|
||||
# Subscribe to tick data from DataProvider
|
||||
self.data_provider_subscriber_id = self.data_provider.subscribe_to_ticks(
|
||||
callback=self._handle_data_provider_tick,
|
||||
symbols=['ETH/USDT', 'BTC/USDT'],
|
||||
subscriber_name="ScalpingDashboard"
|
||||
)
|
||||
logger.info(f"Subscribed to DataProvider tick stream: {self.data_provider_subscriber_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to start DataProvider streaming: {e}")
|
||||
# Fallback to HTTP polling only
|
||||
logger.info("Falling back to HTTP polling only")
|
||||
|
||||
# Always start HTTP polling as backup
|
||||
logger.info("Starting HTTP price polling as backup data source")
|
||||
http_thread = Thread(target=self._http_price_polling, daemon=True)
|
||||
http_thread.start()
|
||||
|
||||
# Start background data refresh thread
|
||||
data_refresh_thread = Thread(target=self._background_data_updater, daemon=True)
|
||||
data_refresh_thread.start()
|
||||
logger.info("Real-time streaming initiated with unified data stream")
|
||||
|
||||
def _handle_data_provider_tick(self, tick: MarketTick):
|
||||
"""Handle tick data from DataProvider"""
|
||||
@ -2283,15 +2150,26 @@ class RealTimeScalpingDashboard:
|
||||
logger.info(f"FIRE: {sofia_time} | Session trading decision: {decision.action} {decision.symbol} @ ${decision.price:.2f}")
|
||||
|
||||
def stop_streaming(self):
|
||||
"""Stop all WebSocket streams"""
|
||||
logger.info("STOP: Stopping real-time WebSocket streams...")
|
||||
"""Stop streaming and cleanup"""
|
||||
logger.info("Stopping dashboard streaming...")
|
||||
|
||||
self.streaming = False
|
||||
|
||||
for thread in self.websocket_threads:
|
||||
if thread.is_alive():
|
||||
thread.join(timeout=2)
|
||||
# Stop unified data stream
|
||||
if hasattr(self, 'unified_stream'):
|
||||
asyncio.run(self.unified_stream.stop_streaming())
|
||||
|
||||
# Unregister as consumer
|
||||
if hasattr(self, 'stream_consumer_id'):
|
||||
self.unified_stream.unregister_consumer(self.stream_consumer_id)
|
||||
|
||||
logger.info("STREAM: WebSocket streams stopped")
|
||||
# Stop any remaining WebSocket threads
|
||||
if hasattr(self, 'websocket_threads'):
|
||||
for thread in self.websocket_threads:
|
||||
if thread.is_alive():
|
||||
thread.join(timeout=2)
|
||||
|
||||
logger.info("Dashboard streaming stopped")
|
||||
|
||||
def run(self, host: str = '127.0.0.1', port: int = 8051, debug: bool = False):
|
||||
"""Run the real-time dashboard"""
|
||||
@ -2486,51 +2364,103 @@ class RealTimeScalpingDashboard:
|
||||
logger.info("ORCHESTRATOR: Enhanced trading loop started with retrospective learning")
|
||||
|
||||
def _start_training_data_collection(self):
|
||||
"""Start training data collection and model training"""
|
||||
"""Start enhanced training data collection using unified stream"""
|
||||
def training_loop():
|
||||
try:
|
||||
logger.info("Training data collection and model training started")
|
||||
logger.info("Enhanced training data collection started with unified stream")
|
||||
|
||||
while True:
|
||||
try:
|
||||
# Collect tick data for training
|
||||
self._collect_training_ticks()
|
||||
# Get latest training data from unified stream
|
||||
training_data = self.unified_stream.get_latest_training_data()
|
||||
|
||||
# Update context data in orchestrator
|
||||
if hasattr(self.orchestrator, 'update_context_data'):
|
||||
self.orchestrator.update_context_data()
|
||||
|
||||
# Initialize extrema trainer if not done
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
if not hasattr(self.orchestrator.extrema_trainer, '_initialized'):
|
||||
self.orchestrator.extrema_trainer.initialize_context_data()
|
||||
self.orchestrator.extrema_trainer._initialized = True
|
||||
logger.info("Extrema trainer context data initialized")
|
||||
|
||||
# Run extrema detection
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
for symbol in self.orchestrator.symbols:
|
||||
detected = self.orchestrator.extrema_trainer.detect_local_extrema(symbol)
|
||||
if detected:
|
||||
logger.info(f"Detected {len(detected)} extrema for {symbol}")
|
||||
|
||||
# Send training data to models periodically
|
||||
if len(self.tick_cache) > 100: # Only when we have enough data
|
||||
self._send_training_data_to_models()
|
||||
if training_data:
|
||||
# Send training data to enhanced RL pipeline
|
||||
self._send_training_data_to_enhanced_rl(training_data)
|
||||
|
||||
# Update context data in orchestrator
|
||||
if hasattr(self.orchestrator, 'update_context_data'):
|
||||
self.orchestrator.update_context_data()
|
||||
|
||||
# Initialize extrema trainer if not done
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
if not hasattr(self.orchestrator.extrema_trainer, '_initialized'):
|
||||
self.orchestrator.extrema_trainer.initialize_context_data()
|
||||
self.orchestrator.extrema_trainer._initialized = True
|
||||
logger.info("Extrema trainer context data initialized")
|
||||
|
||||
# Run extrema detection with real data
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
for symbol in self.orchestrator.symbols:
|
||||
detected = self.orchestrator.extrema_trainer.detect_local_extrema(symbol)
|
||||
if detected:
|
||||
logger.info(f"Detected {len(detected)} extrema for {symbol}")
|
||||
|
||||
time.sleep(30) # Update every 30 seconds
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in training loop: {e}")
|
||||
logger.error(f"Error in enhanced training loop: {e}")
|
||||
time.sleep(10) # Wait before retrying
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Training loop failed: {e}")
|
||||
logger.error(f"Enhanced training loop failed: {e}")
|
||||
|
||||
# Start training thread
|
||||
# Start enhanced training thread
|
||||
training_thread = Thread(target=training_loop, daemon=True)
|
||||
training_thread.start()
|
||||
logger.info("Training data collection thread started")
|
||||
logger.info("Enhanced training data collection thread started")
|
||||
|
||||
def _send_training_data_to_enhanced_rl(self, training_data: TrainingDataPacket):
|
||||
"""Send training data to enhanced RL training pipeline"""
|
||||
try:
|
||||
if not self.orchestrator:
|
||||
return
|
||||
|
||||
# Extract comprehensive training data
|
||||
market_state = training_data.market_state
|
||||
universal_stream = training_data.universal_stream
|
||||
|
||||
if market_state and universal_stream:
|
||||
# Send to enhanced RL trainer if available
|
||||
if hasattr(self.orchestrator, 'enhanced_rl_trainer'):
|
||||
# Create RL training step with comprehensive data
|
||||
asyncio.run(self.orchestrator.enhanced_rl_trainer.training_step(universal_stream))
|
||||
logger.debug("Sent comprehensive data to enhanced RL trainer")
|
||||
|
||||
# Send to extrema trainer for CNN training
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
extrema_data = self.orchestrator.extrema_trainer.get_extrema_training_data(count=50)
|
||||
perfect_moves = self.orchestrator.extrema_trainer.get_perfect_moves_for_cnn(count=100)
|
||||
|
||||
if extrema_data:
|
||||
logger.info(f"Enhanced RL: {len(extrema_data)} extrema training samples available")
|
||||
|
||||
if perfect_moves:
|
||||
logger.info(f"Enhanced RL: {len(perfect_moves)} perfect moves for CNN training")
|
||||
|
||||
# Send to sensitivity learning DQN
|
||||
if hasattr(self.orchestrator, 'sensitivity_learning_queue') and len(self.orchestrator.sensitivity_learning_queue) > 0:
|
||||
logger.info("Enhanced RL: Sensitivity learning data available for DQN training")
|
||||
|
||||
# Get context features for models with real data
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
for symbol in self.orchestrator.symbols:
|
||||
context_features = self.orchestrator.extrema_trainer.get_context_features_for_model(symbol)
|
||||
if context_features is not None:
|
||||
logger.debug(f"Enhanced RL: Context features available for {symbol}: {context_features.shape}")
|
||||
|
||||
# Log training data statistics
|
||||
logger.info(f"Enhanced RL Training Data:")
|
||||
logger.info(f" Tick cache: {len(training_data.tick_cache)} ticks")
|
||||
logger.info(f" 1s bars: {len(training_data.one_second_bars)} bars")
|
||||
logger.info(f" Multi-timeframe data: {len(training_data.multi_timeframe_data)} symbols")
|
||||
logger.info(f" CNN features: {'Available' if training_data.cnn_features else 'Not available'}")
|
||||
logger.info(f" CNN predictions: {'Available' if training_data.cnn_predictions else 'Not available'}")
|
||||
logger.info(f" Market state: {'Available' if training_data.market_state else 'Not available'}")
|
||||
logger.info(f" Universal stream: {'Available' if training_data.universal_stream else 'Not available'}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error sending training data to enhanced RL: {e}")
|
||||
|
||||
def _collect_training_ticks(self):
|
||||
"""Collect real tick data for training cache from data provider"""
|
||||
@ -2607,6 +2537,35 @@ class RealTimeScalpingDashboard:
|
||||
except Exception as e:
|
||||
logger.error(f"Error sending training data to models: {e}")
|
||||
|
||||
def _handle_unified_stream_data(self, data_packet: Dict[str, Any]):
|
||||
"""Handle data from unified stream"""
|
||||
try:
|
||||
# Extract UI data
|
||||
if 'ui_data' in data_packet:
|
||||
self.latest_ui_data = data_packet['ui_data']
|
||||
self.current_prices = self.latest_ui_data.current_prices
|
||||
self.is_streaming = self.latest_ui_data.streaming_status == 'LIVE'
|
||||
self.training_data_available = self.latest_ui_data.training_data_available
|
||||
|
||||
# Extract training data
|
||||
if 'training_data' in data_packet:
|
||||
self.latest_training_data = data_packet['training_data']
|
||||
|
||||
# Extract tick data
|
||||
if 'ticks' in data_packet:
|
||||
ticks = data_packet['ticks']
|
||||
for tick in ticks[-100:]: # Keep last 100 ticks
|
||||
self.tick_cache.append(tick)
|
||||
|
||||
# Extract OHLCV data
|
||||
if 'one_second_bars' in data_packet:
|
||||
bars = data_packet['one_second_bars']
|
||||
for bar in bars[-100:]: # Keep last 100 bars
|
||||
self.one_second_bars.append(bar)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error handling unified stream data: {e}")
|
||||
|
||||
def create_scalping_dashboard(data_provider=None, orchestrator=None, trading_executor=None):
|
||||
"""Create real-time dashboard instance with MEXC integration"""
|
||||
return RealTimeScalpingDashboard(data_provider, orchestrator, trading_executor)
|
||||
|
||||
Reference in New Issue
Block a user