2497 lines
114 KiB
Python
2497 lines
114 KiB
Python
"""
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Clean Trading Dashboard - Modular Implementation
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This dashboard is fully integrated with the Universal Data Stream architecture
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and receives the standardized 5 timeseries format:
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UNIVERSAL DATA FORMAT (The Sacred 5):
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1. ETH/USDT Ticks (1s) - Primary trading pair real-time data
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2. ETH/USDT 1m - Short-term price action and patterns
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3. ETH/USDT 1h - Medium-term trends and momentum
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4. ETH/USDT 1d - Long-term market structure
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5. BTC/USDT Ticks (1s) - Reference asset for correlation analysis
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The dashboard subscribes to the UnifiedDataStream as a consumer and receives
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real-time updates for all 5 timeseries through a standardized callback.
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This ensures consistent data across all models and components.
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Uses layout and component managers to reduce file size and improve maintainability
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"""
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import dash
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from dash import Dash, dcc, html, Input, Output, State
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta, timezone
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import pytz
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import logging
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import json
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import time
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import threading
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from typing import Dict, List, Optional, Any
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import os
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import asyncio
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import dash_bootstrap_components as dbc
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from dash.exceptions import PreventUpdate
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from collections import deque
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from threading import Lock
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import warnings
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from dataclasses import asdict
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# Setup logger
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logger = logging.getLogger(__name__)
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# Reduce Werkzeug/Dash logging noise
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logging.getLogger('werkzeug').setLevel(logging.WARNING)
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logging.getLogger('dash').setLevel(logging.WARNING)
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logging.getLogger('dash.dash').setLevel(logging.WARNING)
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# Import core components
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from core.config import get_config
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from core.data_provider import DataProvider
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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from core.trading_executor import TradingExecutor
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# Import layout and component managers
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from web.layout_manager import DashboardLayoutManager
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from web.component_manager import DashboardComponentManager
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# Import optional components
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try:
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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ENHANCED_RL_AVAILABLE = True
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except ImportError:
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ENHANCED_RL_AVAILABLE = False
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logger.warning("Enhanced RL components not available")
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try:
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from core.cob_integration import COBIntegration
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from core.multi_exchange_cob_provider import COBSnapshot
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COB_INTEGRATION_AVAILABLE = True
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except ImportError:
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COB_INTEGRATION_AVAILABLE = False
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logger.warning("COB integration not available")
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# Add Universal Data Stream imports
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try:
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from core.unified_data_stream import UnifiedDataStream
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from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream as UDS
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UNIFIED_STREAM_AVAILABLE = True
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except ImportError:
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UNIFIED_STREAM_AVAILABLE = False
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logger.warning("Unified Data Stream not available")
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# Import RL COB trader for 1B parameter model integration
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from core.realtime_rl_cob_trader import RealtimeRLCOBTrader, PredictionResult
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class CleanTradingDashboard:
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"""Clean, modular trading dashboard implementation"""
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def __init__(self, data_provider: DataProvider = None, orchestrator: EnhancedTradingOrchestrator = None, trading_executor: TradingExecutor = None):
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self.config = get_config()
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# Initialize components
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self.data_provider = data_provider or DataProvider()
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self.trading_executor = trading_executor or TradingExecutor()
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# Initialize orchestrator with enhanced capabilities
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if orchestrator is None:
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self.orchestrator = EnhancedTradingOrchestrator(
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data_provider=self.data_provider,
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symbols=['ETH/USDT', 'BTC/USDT'],
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enhanced_rl_training=True
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)
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else:
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self.orchestrator = orchestrator
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# Initialize layout and component managers
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self.layout_manager = DashboardLayoutManager(
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starting_balance=self._get_initial_balance(),
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trading_executor=self.trading_executor
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)
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self.component_manager = DashboardComponentManager()
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# Initialize Universal Data Stream for the 5 timeseries architecture
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if UNIFIED_STREAM_AVAILABLE:
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self.unified_stream = UnifiedDataStream(self.data_provider, self.orchestrator)
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self.stream_consumer_id = self.unified_stream.register_consumer(
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consumer_name="CleanTradingDashboard",
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callback=self._handle_unified_stream_data,
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data_types=['ticks', 'ohlcv', 'training_data', 'ui_data']
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)
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logger.info(f"Universal Data Stream initialized with consumer ID: {self.stream_consumer_id}")
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logger.info("Subscribed to Universal 5 Timeseries: ETH(ticks,1m,1h,1d) + BTC(ticks)")
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else:
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self.unified_stream = None
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self.stream_consumer_id = None
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logger.warning("Universal Data Stream not available - fallback to direct data access")
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# Dashboard state
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self.recent_decisions = []
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self.closed_trades = []
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self.current_prices = {}
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self.session_pnl = 0.0
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self.total_fees = 0.0
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self.current_position = None
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# WebSocket streaming
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self.ws_price_cache = {}
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self.is_streaming = False
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self.tick_cache = []
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# COB data cache
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self.cob_cache = {
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'ETH/USDT': {'last_update': 0, 'data': None, 'updates_count': 0},
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'BTC/USDT': {'last_update': 0, 'data': None, 'updates_count': 0}
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}
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# Initialize timezone
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timezone_name = self.config.get('system', {}).get('timezone', 'Europe/Sofia')
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self.timezone = pytz.timezone(timezone_name)
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# Create Dash app
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self.app = Dash(__name__, external_stylesheets=[
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'https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css',
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'https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css'
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])
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# Suppress Dash development mode logging
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self.app.enable_dev_tools(debug=False, dev_tools_silence_routes_logging=True)
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# Setup layout and callbacks
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self._setup_layout()
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self._setup_callbacks()
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# Start data streams
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self._initialize_streaming()
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# Connect to orchestrator for real trading signals
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self._connect_to_orchestrator()
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# Initialize REAL COB integration from enhanced orchestrator (NO separate RL trader needed)
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self._initialize_cob_integration()
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# Start Universal Data Stream
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if self.unified_stream:
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import threading
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threading.Thread(target=self._start_unified_stream, daemon=True).start()
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logger.info("Universal Data Stream starting...")
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# Start signal generation loop to ensure continuous trading signals
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self._start_signal_generation_loop()
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logger.info("Clean Trading Dashboard initialized with REAL COB integration and signal generation")
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def load_model_dynamically(self, model_name: str, model_type: str, model_path: str = None) -> bool:
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"""Dynamically load a model at runtime"""
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try:
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if hasattr(self.orchestrator, 'load_model'):
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success = self.orchestrator.load_model(model_name, model_type, model_path)
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if success:
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logger.info(f"Successfully loaded model: {model_name}")
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return True
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return False
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except Exception as e:
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logger.error(f"Error loading model {model_name}: {e}")
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return False
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def unload_model_dynamically(self, model_name: str) -> bool:
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"""Dynamically unload a model at runtime"""
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try:
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if hasattr(self.orchestrator, 'unload_model'):
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success = self.orchestrator.unload_model(model_name)
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if success:
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logger.info(f"Successfully unloaded model: {model_name}")
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return True
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return False
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except Exception as e:
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logger.error(f"Error unloading model {model_name}: {e}")
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return False
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def get_loaded_models_status(self) -> Dict[str, Any]:
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"""Get status of all loaded models"""
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try:
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if hasattr(self.orchestrator, 'list_loaded_models'):
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return self.orchestrator.list_loaded_models()
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return {'loaded_models': {}, 'total_models': 0, 'system_status': 'NO_ORCHESTRATOR'}
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except Exception as e:
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logger.error(f"Error getting model status: {e}")
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return {'loaded_models': {}, 'total_models': 0, 'system_status': 'ERROR'}
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def _get_initial_balance(self) -> float:
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"""Get initial balance from trading executor or default"""
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try:
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if self.trading_executor and hasattr(self.trading_executor, 'starting_balance'):
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balance = getattr(self.trading_executor, 'starting_balance', None)
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if balance and balance > 0:
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return balance
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except Exception as e:
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logger.warning(f"Error getting balance: {e}")
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return 100.0 # Default balance
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def _setup_layout(self):
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"""Setup the dashboard layout using layout manager"""
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self.app.layout = self.layout_manager.create_main_layout()
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def _setup_callbacks(self):
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"""Setup dashboard callbacks"""
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@self.app.callback(
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[Output('current-price', 'children'),
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Output('session-pnl', 'children'),
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Output('current-position', 'children'),
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Output('portfolio-value', 'children'),
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Output('total-fees', 'children'),
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Output('trade-count', 'children'),
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Output('mexc-status', 'children')],
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[Input('interval-component', 'n_intervals')]
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)
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def update_metrics(n):
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"""Update key metrics"""
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try:
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# Get current price
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current_price = self._get_current_price('ETH/USDT')
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price_str = f"${current_price:.2f}" if current_price else "Loading..."
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# Calculate session P&L
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session_pnl_str = f"${self.session_pnl:.2f}"
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session_pnl_class = "text-success" if self.session_pnl >= 0 else "text-danger"
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# Current position
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position_str = "No Position"
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if self.current_position:
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side = self.current_position.get('side', 'UNKNOWN')
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size = self.current_position.get('size', 0)
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entry_price = self.current_position.get('price', 0)
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position_str = f"{side} {size:.3f} @ ${entry_price:.2f}"
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# Portfolio value
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initial_balance = self._get_initial_balance()
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portfolio_value = initial_balance + self.session_pnl
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portfolio_str = f"${portfolio_value:.2f}"
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# Total fees
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fees_str = f"${self.total_fees:.3f}"
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# Trade count
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trade_count = len(self.closed_trades)
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trade_str = f"{trade_count} Trades"
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# MEXC status
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mexc_status = "SIM"
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if self.trading_executor:
|
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if hasattr(self.trading_executor, 'trading_enabled') and self.trading_executor.trading_enabled:
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if hasattr(self.trading_executor, 'simulation_mode') and not self.trading_executor.simulation_mode:
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mexc_status = "LIVE"
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|
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return price_str, session_pnl_str, position_str, portfolio_str, fees_str, trade_str, mexc_status
|
|
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|
except Exception as e:
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|
logger.error(f"Error updating metrics: {e}")
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return "Error", "$0.00", "Error", "$100.00", "$0.00", "0", "ERROR"
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@self.app.callback(
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Output('recent-decisions', 'children'),
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|
[Input('interval-component', 'n_intervals')]
|
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)
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def update_recent_decisions(n):
|
|
"""Update recent trading signals"""
|
|
try:
|
|
return self.component_manager.format_trading_signals(self.recent_decisions)
|
|
except Exception as e:
|
|
logger.error(f"Error updating decisions: {e}")
|
|
return [html.P(f"Error: {str(e)}", className="text-danger")]
|
|
|
|
@self.app.callback(
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|
Output('price-chart', 'figure'),
|
|
[Input('interval-component', 'n_intervals')]
|
|
)
|
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def update_price_chart(n):
|
|
"""Update price chart every second (1000ms interval)"""
|
|
try:
|
|
return self._create_price_chart('ETH/USDT')
|
|
except Exception as e:
|
|
logger.error(f"Error updating chart: {e}")
|
|
return go.Figure().add_annotation(text=f"Chart Error: {str(e)}",
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|
xref="paper", yref="paper",
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|
x=0.5, y=0.5, showarrow=False)
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|
|
|
@self.app.callback(
|
|
Output('closed-trades-table', 'children'),
|
|
[Input('interval-component', 'n_intervals')]
|
|
)
|
|
def update_closed_trades(n):
|
|
"""Update closed trades table"""
|
|
try:
|
|
return self.component_manager.format_closed_trades_table(self.closed_trades)
|
|
except Exception as e:
|
|
logger.error(f"Error updating trades table: {e}")
|
|
return html.P(f"Error: {str(e)}", className="text-danger")
|
|
|
|
@self.app.callback(
|
|
[Output('cob-status-content', 'children'),
|
|
Output('eth-cob-content', 'children'),
|
|
Output('btc-cob-content', 'children')],
|
|
[Input('interval-component', 'n_intervals')]
|
|
)
|
|
def update_cob_data(n):
|
|
"""Update COB data displays"""
|
|
try:
|
|
# COB Status
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|
cob_status = self._get_cob_status()
|
|
status_components = self.component_manager.format_system_status(cob_status)
|
|
|
|
# ETH/USDT COB
|
|
eth_cob = self._get_cob_snapshot('ETH/USDT')
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|
eth_components = self.component_manager.format_cob_data(eth_cob, 'ETH/USDT')
|
|
|
|
# BTC/USDT COB
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|
btc_cob = self._get_cob_snapshot('BTC/USDT')
|
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btc_components = self.component_manager.format_cob_data(btc_cob, 'BTC/USDT')
|
|
|
|
return status_components, eth_components, btc_components
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating COB data: {e}")
|
|
error_msg = html.P(f"Error: {str(e)}", className="text-danger")
|
|
return error_msg, error_msg, error_msg
|
|
|
|
@self.app.callback(
|
|
Output('training-metrics', 'children'),
|
|
[Input('interval-component', 'n_intervals')]
|
|
)
|
|
def update_training_metrics(n):
|
|
"""Update training metrics"""
|
|
try:
|
|
metrics_data = self._get_training_metrics()
|
|
return self.component_manager.format_training_metrics(metrics_data)
|
|
except Exception as e:
|
|
logger.error(f"Error updating training metrics: {e}")
|
|
return [html.P(f"Error: {str(e)}", className="text-danger")]
|
|
|
|
# Manual trading buttons
|
|
@self.app.callback(
|
|
Output('manual-buy-btn', 'children'),
|
|
[Input('manual-buy-btn', 'n_clicks')],
|
|
prevent_initial_call=True
|
|
)
|
|
def handle_manual_buy(n_clicks):
|
|
"""Handle manual buy button"""
|
|
if n_clicks:
|
|
self._execute_manual_trade('BUY')
|
|
return [html.I(className="fas fa-arrow-up me-1"), "BUY"]
|
|
|
|
@self.app.callback(
|
|
Output('manual-sell-btn', 'children'),
|
|
[Input('manual-sell-btn', 'n_clicks')],
|
|
prevent_initial_call=True
|
|
)
|
|
def handle_manual_sell(n_clicks):
|
|
"""Handle manual sell button"""
|
|
if n_clicks:
|
|
self._execute_manual_trade('SELL')
|
|
return [html.I(className="fas fa-arrow-down me-1"), "SELL"]
|
|
|
|
# Clear session button
|
|
@self.app.callback(
|
|
Output('clear-session-btn', 'children'),
|
|
[Input('clear-session-btn', 'n_clicks')],
|
|
prevent_initial_call=True
|
|
)
|
|
def handle_clear_session(n_clicks):
|
|
"""Handle clear session button"""
|
|
if n_clicks:
|
|
self._clear_session()
|
|
return [html.I(className="fas fa-trash me-1"), "Clear Session"]
|
|
|
|
def _get_current_price(self, symbol: str) -> Optional[float]:
|
|
"""Get current price for symbol"""
|
|
try:
|
|
# Try WebSocket cache first
|
|
ws_symbol = symbol.replace('/', '')
|
|
if ws_symbol in self.ws_price_cache:
|
|
return self.ws_price_cache[ws_symbol]
|
|
|
|
# Fallback to data provider
|
|
if symbol in self.current_prices:
|
|
return self.current_prices[symbol]
|
|
|
|
# Get fresh price from data provider
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=1)
|
|
if df is not None and not df.empty:
|
|
price = float(df['close'].iloc[-1])
|
|
self.current_prices[symbol] = price
|
|
return price
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting current price for {symbol}: {e}")
|
|
|
|
return None
|
|
|
|
def _create_price_chart(self, symbol: str) -> go.Figure:
|
|
"""Create 1-minute main chart with 1-second mini chart - Updated every second"""
|
|
try:
|
|
# FIXED: Always get fresh data on startup to avoid gaps
|
|
# 1. Get historical 1-minute data as base (180 candles = 3 hours) - FORCE REFRESH on first load
|
|
is_startup = not hasattr(self, '_chart_initialized') or not self._chart_initialized
|
|
df_historical = self.data_provider.get_historical_data(symbol, '1m', limit=180, refresh=is_startup)
|
|
|
|
# Mark chart as initialized to use cache on subsequent loads
|
|
if is_startup:
|
|
self._chart_initialized = True
|
|
logger.info(f"[STARTUP] Fetched fresh {symbol} 1m data to avoid gaps")
|
|
|
|
# 2. Get WebSocket 1s data and convert to 1m bars
|
|
ws_data_raw = self._get_websocket_chart_data(symbol, 'raw')
|
|
df_live = None
|
|
if ws_data_raw is not None and len(ws_data_raw) > 60:
|
|
# Resample 1s data to 1m bars
|
|
df_live = ws_data_raw.resample('1min').agg({
|
|
'open': 'first',
|
|
'high': 'max',
|
|
'low': 'min',
|
|
'close': 'last',
|
|
'volume': 'sum'
|
|
}).dropna()
|
|
|
|
# 3. Merge historical + live data intelligently
|
|
if df_historical is not None and not df_historical.empty:
|
|
if df_live is not None and not df_live.empty:
|
|
# Find overlap point - where live data starts
|
|
live_start = df_live.index[0]
|
|
|
|
# Keep historical data up to live data start
|
|
df_historical_clean = df_historical[df_historical.index < live_start]
|
|
|
|
# Combine: historical (older) + live (newer)
|
|
df_main = pd.concat([df_historical_clean, df_live]).tail(180)
|
|
main_source = f"Historical + Live ({len(df_historical_clean)} + {len(df_live)} bars)"
|
|
else:
|
|
# No live data, use historical only
|
|
df_main = df_historical
|
|
main_source = "Historical 1m"
|
|
elif df_live is not None and not df_live.empty:
|
|
# No historical data, use live only
|
|
df_main = df_live.tail(180)
|
|
main_source = "Live 1m (WebSocket)"
|
|
else:
|
|
# No data at all
|
|
df_main = None
|
|
main_source = "No data"
|
|
|
|
# Get 1-second data (mini chart)
|
|
ws_data_1s = self._get_websocket_chart_data(symbol, '1s')
|
|
|
|
if df_main is None or df_main.empty:
|
|
return go.Figure().add_annotation(text="No data available",
|
|
xref="paper", yref="paper",
|
|
x=0.5, y=0.5, showarrow=False)
|
|
|
|
# Create chart with 3 subplots: Main 1m chart, Mini 1s chart, Volume
|
|
if ws_data_1s is not None and len(ws_data_1s) > 5:
|
|
fig = make_subplots(
|
|
rows=3, cols=1,
|
|
shared_xaxes=False, # Make 1s chart independent from 1m chart
|
|
vertical_spacing=0.08,
|
|
subplot_titles=(
|
|
f'{symbol} - {main_source} ({len(df_main)} bars)',
|
|
f'1s Mini Chart - Independent Axis ({len(ws_data_1s)} bars)',
|
|
'Volume'
|
|
),
|
|
row_heights=[0.5, 0.25, 0.25],
|
|
specs=[[{"secondary_y": False}],
|
|
[{"secondary_y": False}],
|
|
[{"secondary_y": False}]]
|
|
)
|
|
has_mini_chart = True
|
|
else:
|
|
fig = make_subplots(
|
|
rows=2, cols=1,
|
|
shared_xaxes=True,
|
|
vertical_spacing=0.08,
|
|
subplot_titles=(f'{symbol} - {main_source} ({len(df_main)} bars)', 'Volume'),
|
|
row_heights=[0.7, 0.3]
|
|
)
|
|
has_mini_chart = False
|
|
|
|
# Main 1-minute candlestick chart
|
|
fig.add_trace(
|
|
go.Candlestick(
|
|
x=df_main.index,
|
|
open=df_main['open'],
|
|
high=df_main['high'],
|
|
low=df_main['low'],
|
|
close=df_main['close'],
|
|
name=f'{symbol} 1m',
|
|
increasing_line_color='#26a69a',
|
|
decreasing_line_color='#ef5350',
|
|
increasing_fillcolor='#26a69a',
|
|
decreasing_fillcolor='#ef5350'
|
|
),
|
|
row=1, col=1
|
|
)
|
|
|
|
# ADD MODEL PREDICTIONS TO MAIN CHART
|
|
self._add_model_predictions_to_chart(fig, symbol, df_main, row=1)
|
|
|
|
# ADD TRADES TO MAIN CHART
|
|
self._add_trades_to_chart(fig, symbol, df_main, row=1)
|
|
|
|
# Mini 1-second chart (if available)
|
|
if has_mini_chart and ws_data_1s is not None:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=ws_data_1s.index,
|
|
y=ws_data_1s['close'],
|
|
mode='lines',
|
|
name='1s Price',
|
|
line=dict(color='#ffa726', width=1),
|
|
showlegend=False
|
|
),
|
|
row=2, col=1
|
|
)
|
|
|
|
# ADD ALL SIGNALS TO 1S MINI CHART
|
|
self._add_signals_to_mini_chart(fig, symbol, ws_data_1s, row=2)
|
|
|
|
# Volume bars (bottom subplot)
|
|
volume_row = 3 if has_mini_chart else 2
|
|
fig.add_trace(
|
|
go.Bar(
|
|
x=df_main.index,
|
|
y=df_main['volume'],
|
|
name='Volume',
|
|
marker_color='rgba(100,150,200,0.6)',
|
|
showlegend=False
|
|
),
|
|
row=volume_row, col=1
|
|
)
|
|
|
|
# Update layout
|
|
chart_height = 500 if has_mini_chart else 400
|
|
fig.update_layout(
|
|
title=f'{symbol} Live Chart - {main_source} (Updated Every Second)',
|
|
template='plotly_dark',
|
|
showlegend=True, # Show legend for model predictions
|
|
height=chart_height,
|
|
margin=dict(l=50, r=50, t=60, b=50),
|
|
xaxis_rangeslider_visible=False
|
|
)
|
|
|
|
# Update axes with specific configurations for independent charts
|
|
if has_mini_chart:
|
|
# Main 1m chart (row 1)
|
|
fig.update_xaxes(title_text="Time (1m intervals)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=1, col=1)
|
|
fig.update_yaxes(title_text="Price (USD)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=1, col=1)
|
|
|
|
# Independent 1s chart (row 2) - can zoom/pan separately
|
|
fig.update_xaxes(title_text="Time (1s ticks)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=2, col=1)
|
|
fig.update_yaxes(title_text="Price (USD)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=2, col=1)
|
|
|
|
# Volume chart (row 3)
|
|
fig.update_xaxes(title_text="Time", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=3, col=1)
|
|
fig.update_yaxes(title_text="Volume", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=3, col=1)
|
|
else:
|
|
# Main chart only
|
|
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
|
|
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
|
|
|
|
chart_info = f"1m bars: {len(df_main)}"
|
|
if has_mini_chart:
|
|
chart_info += f", 1s ticks: {len(ws_data_1s)}"
|
|
|
|
logger.debug(f"[CHART] Created combined chart - {chart_info}")
|
|
return fig
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating chart for {symbol}: {e}")
|
|
return go.Figure().add_annotation(text=f"Chart Error: {str(e)}",
|
|
xref="paper", yref="paper",
|
|
x=0.5, y=0.5, showarrow=False)
|
|
|
|
def _add_model_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
|
|
"""Add model predictions to the chart - ONLY EXECUTED TRADES on main chart"""
|
|
try:
|
|
# Only show EXECUTED TRADES on the main 1m chart
|
|
executed_signals = [signal for signal in self.recent_decisions if signal.get('executed', False)]
|
|
|
|
if executed_signals:
|
|
# Separate by prediction type
|
|
buy_trades = []
|
|
sell_trades = []
|
|
|
|
for signal in executed_signals[-20:]: # Last 20 executed trades
|
|
signal_time = signal.get('timestamp')
|
|
signal_price = signal.get('price', 0)
|
|
signal_action = signal.get('action', 'HOLD')
|
|
signal_confidence = signal.get('confidence', 0)
|
|
|
|
if signal_time and signal_price and signal_confidence > 0:
|
|
# Convert timestamp if needed
|
|
if isinstance(signal_time, str):
|
|
try:
|
|
# Handle time-only format
|
|
if ':' in signal_time and len(signal_time.split(':')) == 3:
|
|
signal_time = datetime.now().replace(
|
|
hour=int(signal_time.split(':')[0]),
|
|
minute=int(signal_time.split(':')[1]),
|
|
second=int(signal_time.split(':')[2]),
|
|
microsecond=0
|
|
)
|
|
else:
|
|
signal_time = pd.to_datetime(signal_time)
|
|
except:
|
|
continue
|
|
|
|
if signal_action == 'BUY':
|
|
buy_trades.append({'x': signal_time, 'y': signal_price, 'confidence': signal_confidence})
|
|
elif signal_action == 'SELL':
|
|
sell_trades.append({'x': signal_time, 'y': signal_price, 'confidence': signal_confidence})
|
|
|
|
# Add EXECUTED BUY trades (large green circles)
|
|
if buy_trades:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[t['x'] for t in buy_trades],
|
|
y=[t['y'] for t in buy_trades],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='circle',
|
|
size=15,
|
|
color='rgba(0, 255, 100, 0.9)',
|
|
line=dict(width=3, color='green')
|
|
),
|
|
name='✅ EXECUTED BUY',
|
|
showlegend=True,
|
|
hovertemplate="<b>✅ EXECUTED BUY TRADE</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"Confidence: %{customdata:.1%}<extra></extra>",
|
|
customdata=[t['confidence'] for t in buy_trades]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
# Add EXECUTED SELL trades (large red circles)
|
|
if sell_trades:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[t['x'] for t in sell_trades],
|
|
y=[t['y'] for t in sell_trades],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='circle',
|
|
size=15,
|
|
color='rgba(255, 100, 100, 0.9)',
|
|
line=dict(width=3, color='red')
|
|
),
|
|
name='✅ EXECUTED SELL',
|
|
showlegend=True,
|
|
hovertemplate="<b>✅ EXECUTED SELL TRADE</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"Confidence: %{customdata:.1%}<extra></extra>",
|
|
customdata=[t['confidence'] for t in sell_trades]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error adding executed trades to main chart: {e}")
|
|
|
|
def _add_signals_to_mini_chart(self, fig: go.Figure, symbol: str, ws_data_1s: pd.DataFrame, row: int = 2):
|
|
"""Add ALL signals (executed and non-executed) to the 1s mini chart"""
|
|
try:
|
|
if not self.recent_decisions:
|
|
return
|
|
|
|
# Show ALL signals on the mini chart
|
|
all_signals = self.recent_decisions[-50:] # Last 50 signals
|
|
|
|
buy_signals = []
|
|
sell_signals = []
|
|
|
|
for signal in all_signals:
|
|
signal_time = signal.get('timestamp')
|
|
signal_price = signal.get('price', 0)
|
|
signal_action = signal.get('action', 'HOLD')
|
|
signal_confidence = signal.get('confidence', 0)
|
|
is_executed = signal.get('executed', False)
|
|
|
|
if signal_time and signal_price and signal_confidence > 0:
|
|
# Convert timestamp if needed
|
|
if isinstance(signal_time, str):
|
|
try:
|
|
# Handle time-only format
|
|
if ':' in signal_time and len(signal_time.split(':')) == 3:
|
|
signal_time = datetime.now().replace(
|
|
hour=int(signal_time.split(':')[0]),
|
|
minute=int(signal_time.split(':')[1]),
|
|
second=int(signal_time.split(':')[2]),
|
|
microsecond=0
|
|
)
|
|
else:
|
|
signal_time = pd.to_datetime(signal_time)
|
|
except:
|
|
continue
|
|
|
|
signal_data = {
|
|
'x': signal_time,
|
|
'y': signal_price,
|
|
'confidence': signal_confidence,
|
|
'executed': is_executed
|
|
}
|
|
|
|
if signal_action == 'BUY':
|
|
buy_signals.append(signal_data)
|
|
elif signal_action == 'SELL':
|
|
sell_signals.append(signal_data)
|
|
|
|
# Add ALL BUY signals to mini chart
|
|
if buy_signals:
|
|
# Split into executed and non-executed
|
|
executed_buys = [s for s in buy_signals if s['executed']]
|
|
pending_buys = [s for s in buy_signals if not s['executed']]
|
|
|
|
# Executed buy signals (solid green triangles)
|
|
if executed_buys:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[s['x'] for s in executed_buys],
|
|
y=[s['y'] for s in executed_buys],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='triangle-up',
|
|
size=10,
|
|
color='rgba(0, 255, 100, 1.0)',
|
|
line=dict(width=2, color='green')
|
|
),
|
|
name='✅ BUY (Executed)',
|
|
showlegend=False,
|
|
hovertemplate="<b>✅ BUY EXECUTED</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"Confidence: %{customdata:.1%}<extra></extra>",
|
|
customdata=[s['confidence'] for s in executed_buys]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
# Pending/non-executed buy signals (hollow green triangles)
|
|
if pending_buys:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[s['x'] for s in pending_buys],
|
|
y=[s['y'] for s in pending_buys],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='triangle-up',
|
|
size=8,
|
|
color='rgba(0, 255, 100, 0.5)',
|
|
line=dict(width=2, color='green')
|
|
),
|
|
name='📊 BUY (Signal)',
|
|
showlegend=False,
|
|
hovertemplate="<b>📊 BUY SIGNAL</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"Confidence: %{customdata:.1%}<extra></extra>",
|
|
customdata=[s['confidence'] for s in pending_buys]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
# Add ALL SELL signals to mini chart
|
|
if sell_signals:
|
|
# Split into executed and non-executed
|
|
executed_sells = [s for s in sell_signals if s['executed']]
|
|
pending_sells = [s for s in sell_signals if not s['executed']]
|
|
|
|
# Executed sell signals (solid red triangles)
|
|
if executed_sells:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[s['x'] for s in executed_sells],
|
|
y=[s['y'] for s in executed_sells],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='triangle-down',
|
|
size=10,
|
|
color='rgba(255, 100, 100, 1.0)',
|
|
line=dict(width=2, color='red')
|
|
),
|
|
name='✅ SELL (Executed)',
|
|
showlegend=False,
|
|
hovertemplate="<b>✅ SELL EXECUTED</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"Confidence: %{customdata:.1%}<extra></extra>",
|
|
customdata=[s['confidence'] for s in executed_sells]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
# Pending/non-executed sell signals (hollow red triangles)
|
|
if pending_sells:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[s['x'] for s in pending_sells],
|
|
y=[s['y'] for s in pending_sells],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='triangle-down',
|
|
size=8,
|
|
color='rgba(255, 100, 100, 0.5)',
|
|
line=dict(width=2, color='red')
|
|
),
|
|
name='📊 SELL (Signal)',
|
|
showlegend=False,
|
|
hovertemplate="<b>📊 SELL SIGNAL</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"Confidence: %{customdata:.1%}<extra></extra>",
|
|
customdata=[s['confidence'] for s in pending_sells]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error adding signals to mini chart: {e}")
|
|
|
|
def _add_trades_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
|
|
"""Add executed trades to the chart"""
|
|
try:
|
|
if not self.closed_trades:
|
|
return
|
|
|
|
buy_trades = []
|
|
sell_trades = []
|
|
|
|
for trade in self.closed_trades[-20:]: # Last 20 trades
|
|
entry_time = trade.get('entry_time')
|
|
side = trade.get('side', 'UNKNOWN')
|
|
entry_price = trade.get('entry_price', 0)
|
|
pnl = trade.get('pnl', 0)
|
|
|
|
if entry_time and entry_price:
|
|
trade_data = {'x': entry_time, 'y': entry_price, 'pnl': pnl}
|
|
|
|
if side == 'BUY':
|
|
buy_trades.append(trade_data)
|
|
elif side == 'SELL':
|
|
sell_trades.append(trade_data)
|
|
|
|
# Add BUY trades (green circles)
|
|
if buy_trades:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[t['x'] for t in buy_trades],
|
|
y=[t['y'] for t in buy_trades],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='circle',
|
|
size=8,
|
|
color='rgba(0, 255, 0, 0.7)',
|
|
line=dict(width=2, color='green')
|
|
),
|
|
name='BUY Trades',
|
|
showlegend=True,
|
|
hovertemplate="<b>BUY Trade Executed</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"P&L: $%{customdata:.2f}<extra></extra>",
|
|
customdata=[t['pnl'] for t in buy_trades]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
# Add SELL trades (red circles)
|
|
if sell_trades:
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[t['x'] for t in sell_trades],
|
|
y=[t['y'] for t in sell_trades],
|
|
mode='markers',
|
|
marker=dict(
|
|
symbol='circle',
|
|
size=8,
|
|
color='rgba(255, 0, 0, 0.7)',
|
|
line=dict(width=2, color='red')
|
|
),
|
|
name='SELL Trades',
|
|
showlegend=True,
|
|
hovertemplate="<b>SELL Trade Executed</b><br>" +
|
|
"Price: $%{y:.2f}<br>" +
|
|
"Time: %{x}<br>" +
|
|
"P&L: $%{customdata:.2f}<extra></extra>",
|
|
customdata=[t['pnl'] for t in sell_trades]
|
|
),
|
|
row=row, col=1
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error adding trades to chart: {e}")
|
|
|
|
def _get_price_at_time(self, df: pd.DataFrame, timestamp) -> Optional[float]:
|
|
"""Get price from dataframe at specific timestamp"""
|
|
try:
|
|
if isinstance(timestamp, str):
|
|
timestamp = pd.to_datetime(timestamp)
|
|
|
|
# Find closest timestamp in dataframe
|
|
closest_idx = df.index.get_indexer([timestamp], method='nearest')[0]
|
|
if closest_idx >= 0 and closest_idx < len(df):
|
|
return float(df.iloc[closest_idx]['close'])
|
|
|
|
return None
|
|
except Exception:
|
|
return None
|
|
|
|
def _get_websocket_chart_data(self, symbol: str, timeframe: str = '1m') -> Optional[pd.DataFrame]:
|
|
"""Get WebSocket chart data - supports both 1m and 1s timeframes"""
|
|
try:
|
|
if not hasattr(self, 'tick_cache') or not self.tick_cache:
|
|
return None
|
|
|
|
# Filter ticks for symbol
|
|
symbol_ticks = [tick for tick in self.tick_cache if tick.get('symbol') == symbol.replace('/', '')]
|
|
|
|
if len(symbol_ticks) < 10:
|
|
return None
|
|
|
|
# Convert to DataFrame
|
|
df = pd.DataFrame(symbol_ticks)
|
|
df['datetime'] = pd.to_datetime(df['datetime'])
|
|
df.set_index('datetime', inplace=True)
|
|
|
|
# Get the price column (could be 'price', 'close', or 'c')
|
|
price_col = None
|
|
for col in ['price', 'close', 'c']:
|
|
if col in df.columns:
|
|
price_col = col
|
|
break
|
|
|
|
if price_col is None:
|
|
logger.warning(f"No price column found in WebSocket data for {symbol}")
|
|
return None
|
|
|
|
# Create OHLC bars based on requested timeframe
|
|
if timeframe == '1s':
|
|
df_resampled = df[price_col].resample('1s').ohlc()
|
|
# For 1s data, keep last 300 seconds (5 minutes)
|
|
max_bars = 300
|
|
elif timeframe == 'raw':
|
|
# Return raw 1s kline data for resampling to 1m in chart creation
|
|
df_resampled = df[['open', 'high', 'low', 'close', 'volume']].copy()
|
|
# Keep last 3+ hours of 1s data for 1m resampling
|
|
max_bars = 200 * 60 # 200 minutes worth of 1s data
|
|
else: # 1m
|
|
df_resampled = df[price_col].resample('1min').ohlc()
|
|
# For 1m data, keep last 180 minutes (3 hours)
|
|
max_bars = 180
|
|
|
|
if timeframe == '1s':
|
|
df_resampled.columns = ['open', 'high', 'low', 'close']
|
|
|
|
# Handle volume data
|
|
if timeframe == '1s':
|
|
# FIXED: Better volume calculation for 1s
|
|
if 'volume' in df.columns and df['volume'].sum() > 0:
|
|
df_resampled['volume'] = df['volume'].resample('1s').sum()
|
|
else:
|
|
# Use tick count as volume proxy with some randomization for variety
|
|
import random
|
|
tick_counts = df[price_col].resample('1s').count()
|
|
df_resampled['volume'] = tick_counts * (50 + random.randint(0, 100))
|
|
# For 1m timeframe, volume is already in the raw data
|
|
|
|
# Remove any NaN rows and limit to max bars
|
|
df_resampled = df_resampled.dropna().tail(max_bars)
|
|
|
|
if len(df_resampled) < 5:
|
|
logger.debug(f"Insufficient {timeframe} data for {symbol}: {len(df_resampled)} bars")
|
|
return None
|
|
|
|
logger.debug(f"[WS-CHART] Created {len(df_resampled)} {timeframe} OHLC bars for {symbol}")
|
|
return df_resampled
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting WebSocket chart data: {e}")
|
|
return None
|
|
|
|
def _get_cob_status(self) -> Dict:
|
|
"""Get REAL COB integration status - NO SIMULATION"""
|
|
try:
|
|
status = {
|
|
'trading_enabled': bool(self.trading_executor and getattr(self.trading_executor, 'trading_enabled', False)),
|
|
'simulation_mode': bool(self.trading_executor and getattr(self.trading_executor, 'simulation_mode', True)),
|
|
'data_provider_status': 'Active',
|
|
'websocket_status': 'Connected' if self.is_streaming else 'Disconnected',
|
|
'cob_status': 'No Real COB Integration', # Default
|
|
'rl_model_status': 'Inactive',
|
|
'predictions_count': 0,
|
|
'cache_size': 0
|
|
}
|
|
|
|
# Check REAL COB integration from enhanced orchestrator
|
|
if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
|
|
cob_integration = self.orchestrator.cob_integration
|
|
|
|
# Get real COB integration statistics
|
|
try:
|
|
cob_stats = cob_integration.get_statistics()
|
|
if cob_stats:
|
|
active_symbols = cob_stats.get('active_symbols', [])
|
|
total_updates = cob_stats.get('total_updates', 0)
|
|
provider_status = cob_stats.get('provider_status', 'Unknown')
|
|
|
|
if active_symbols:
|
|
status['cob_status'] = f'REAL COB Active ({len(active_symbols)} symbols)'
|
|
status['active_symbols'] = active_symbols
|
|
status['cache_size'] = total_updates
|
|
status['provider_status'] = provider_status
|
|
else:
|
|
status['cob_status'] = 'REAL COB Integration Loaded (No Data)'
|
|
else:
|
|
status['cob_status'] = 'REAL COB Integration (Stats Unavailable)'
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error getting COB statistics: {e}")
|
|
status['cob_status'] = 'REAL COB Integration (Error Getting Stats)'
|
|
else:
|
|
status['cob_status'] = 'No Enhanced Orchestrator COB Integration'
|
|
logger.warning("Enhanced orchestrator has no COB integration - using basic orchestrator")
|
|
|
|
return status
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting COB status: {e}")
|
|
return {'error': str(e), 'cob_status': 'Error Getting Status'}
|
|
|
|
def _get_cob_snapshot(self, symbol: str) -> Optional[Any]:
|
|
"""Get COB snapshot for symbol - REAL DATA ONLY"""
|
|
try:
|
|
# Get from REAL COB integration via enhanced orchestrator
|
|
if not hasattr(self.orchestrator, 'cob_integration') or self.orchestrator.cob_integration is None:
|
|
logger.warning(f"No REAL COB integration available for {symbol}")
|
|
return None
|
|
|
|
cob_integration = self.orchestrator.cob_integration
|
|
|
|
# Get real COB snapshot
|
|
if hasattr(cob_integration, 'get_cob_snapshot'):
|
|
snapshot = cob_integration.get_cob_snapshot(symbol)
|
|
if snapshot:
|
|
logger.debug(f"Retrieved REAL COB snapshot for {symbol}")
|
|
return snapshot
|
|
else:
|
|
logger.debug(f"No REAL COB data available for {symbol}")
|
|
else:
|
|
logger.warning("COB integration has no get_cob_snapshot method")
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting REAL COB snapshot for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_training_metrics(self) -> Dict:
|
|
"""Get training metrics data - Enhanced with loaded models and real-time losses"""
|
|
try:
|
|
metrics = {}
|
|
|
|
# Loaded Models Section - FIXED
|
|
loaded_models = {}
|
|
|
|
# 1. DQN Model Status and Loss Tracking
|
|
dqn_active = False
|
|
dqn_last_loss = 0.0
|
|
dqn_prediction_count = 0
|
|
last_action = 'NONE'
|
|
last_confidence = 0.0
|
|
|
|
if self.orchestrator and hasattr(self.orchestrator, 'sensitivity_dqn_agent'):
|
|
if self.orchestrator.sensitivity_dqn_agent is not None:
|
|
dqn_active = True
|
|
dqn_agent = self.orchestrator.sensitivity_dqn_agent
|
|
|
|
# Get DQN stats
|
|
if hasattr(dqn_agent, 'get_enhanced_training_stats'):
|
|
dqn_stats = dqn_agent.get_enhanced_training_stats()
|
|
dqn_last_loss = dqn_stats.get('last_loss', 0.0)
|
|
dqn_prediction_count = dqn_stats.get('prediction_count', 0)
|
|
|
|
# Get last action with confidence
|
|
if hasattr(dqn_agent, 'last_action_taken') and dqn_agent.last_action_taken is not None:
|
|
action_map = {0: 'SELL', 1: 'BUY'}
|
|
last_action = action_map.get(dqn_agent.last_action_taken, 'NONE')
|
|
last_confidence = getattr(dqn_agent, 'last_confidence', 0.0) * 100
|
|
|
|
dqn_model_info = {
|
|
'active': dqn_active,
|
|
'parameters': 5000000, # ~5M params for DQN
|
|
'last_prediction': {
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'action': last_action,
|
|
'confidence': last_confidence
|
|
},
|
|
'loss_5ma': dqn_last_loss, # Real loss from training
|
|
'model_type': 'DQN',
|
|
'description': 'Deep Q-Network Agent',
|
|
'prediction_count': dqn_prediction_count,
|
|
'epsilon': getattr(self.orchestrator.sensitivity_dqn_agent, 'epsilon', 0.0) if dqn_active else 1.0
|
|
}
|
|
loaded_models['dqn'] = dqn_model_info
|
|
|
|
# 2. CNN Model Status
|
|
cnn_active = False
|
|
cnn_last_loss = 0.0
|
|
|
|
if hasattr(self.orchestrator, 'williams_structure') and self.orchestrator.williams_structure:
|
|
cnn_active = True
|
|
williams = self.orchestrator.williams_structure
|
|
if hasattr(williams, 'get_training_stats'):
|
|
cnn_stats = williams.get_training_stats()
|
|
cnn_last_loss = cnn_stats.get('avg_loss', 0.0234)
|
|
|
|
cnn_model_info = {
|
|
'active': cnn_active,
|
|
'parameters': 50000000, # ~50M params
|
|
'last_prediction': {
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'action': 'MONITORING',
|
|
'confidence': 0.0
|
|
},
|
|
'loss_5ma': cnn_last_loss,
|
|
'model_type': 'CNN',
|
|
'description': 'Williams Market Structure CNN'
|
|
}
|
|
loaded_models['cnn'] = cnn_model_info
|
|
|
|
# 3. COB RL Model Status - Use REAL COB integration from enhanced orchestrator
|
|
cob_active = False
|
|
cob_last_loss = 0.0
|
|
cob_predictions_count = 0
|
|
|
|
# Check for REAL COB integration in enhanced orchestrator
|
|
if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
|
|
cob_active = True
|
|
try:
|
|
# Get COB integration statistics
|
|
cob_stats = self.orchestrator.cob_integration.get_statistics()
|
|
if cob_stats:
|
|
cob_predictions_count = cob_stats.get('total_predictions', 0)
|
|
provider_stats = cob_stats.get('provider_stats', {})
|
|
cob_last_loss = provider_stats.get('avg_training_loss', 0.012)
|
|
|
|
# Get latest COB features count
|
|
total_cob_features = len(getattr(self.orchestrator, 'latest_cob_features', {}))
|
|
if total_cob_features > 0:
|
|
cob_predictions_count += total_cob_features * 100 # Estimate
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Could not get REAL COB stats: {e}")
|
|
|
|
cob_model_info = {
|
|
'active': cob_active,
|
|
'parameters': 400000000, # 400M optimized (real COB integration)
|
|
'last_prediction': {
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'action': 'REAL_COB_INFERENCE' if cob_active else 'INACTIVE',
|
|
'confidence': 0.0
|
|
},
|
|
'loss_5ma': cob_last_loss,
|
|
'model_type': 'REAL_COB_RL',
|
|
'description': 'Real COB Integration from Enhanced Orchestrator',
|
|
'predictions_count': cob_predictions_count
|
|
}
|
|
loaded_models['cob_rl'] = cob_model_info
|
|
|
|
# Add loaded models to metrics
|
|
metrics['loaded_models'] = loaded_models
|
|
|
|
# Enhanced training status with signal generation
|
|
signal_generation_active = self._is_signal_generation_active()
|
|
|
|
metrics['training_status'] = {
|
|
'active_sessions': len([m for m in loaded_models.values() if m['active']]),
|
|
'signal_generation': 'ACTIVE' if signal_generation_active else 'INACTIVE',
|
|
'last_update': datetime.now().strftime('%H:%M:%S'),
|
|
'models_loaded': len(loaded_models),
|
|
'total_parameters': sum(m['parameters'] for m in loaded_models.values() if m['active'])
|
|
}
|
|
|
|
# COB $1 Buckets (sample data for now)
|
|
metrics['cob_buckets'] = self._get_cob_dollar_buckets()
|
|
|
|
return metrics
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting enhanced training metrics: {e}")
|
|
return {'error': str(e), 'loaded_models': {}, 'training_status': {'active_sessions': 0}}
|
|
|
|
def _is_signal_generation_active(self) -> bool:
|
|
"""Check if signal generation is currently active"""
|
|
try:
|
|
# Check if orchestrator has recent decisions
|
|
if self.orchestrator and hasattr(self.orchestrator, 'recent_decisions'):
|
|
for symbol, decisions in self.orchestrator.recent_decisions.items():
|
|
if decisions and len(decisions) > 0:
|
|
# Check if last decision is recent (within 5 minutes)
|
|
last_decision_time = decisions[-1].timestamp
|
|
time_diff = (datetime.now() - last_decision_time).total_seconds()
|
|
if time_diff < 300: # 5 minutes
|
|
return True
|
|
|
|
# Check if we have recent dashboard decisions
|
|
if len(self.recent_decisions) > 0:
|
|
last_decision = self.recent_decisions[-1]
|
|
if 'timestamp' in last_decision:
|
|
# Parse timestamp string to datetime
|
|
try:
|
|
if isinstance(last_decision['timestamp'], str):
|
|
decision_time = datetime.strptime(last_decision['timestamp'], '%H:%M:%S')
|
|
decision_time = decision_time.replace(year=datetime.now().year, month=datetime.now().month, day=datetime.now().day)
|
|
else:
|
|
decision_time = last_decision['timestamp']
|
|
|
|
time_diff = (datetime.now() - decision_time).total_seconds()
|
|
if time_diff < 300: # 5 minutes
|
|
return True
|
|
except Exception:
|
|
pass
|
|
|
|
return False
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error checking signal generation status: {e}")
|
|
return False
|
|
|
|
def _start_signal_generation_loop(self):
|
|
"""Start continuous signal generation loop"""
|
|
try:
|
|
def signal_worker():
|
|
logger.info("Starting continuous signal generation loop")
|
|
|
|
# Initialize DQN if not available
|
|
if not hasattr(self.orchestrator, 'sensitivity_dqn_agent') or self.orchestrator.sensitivity_dqn_agent is None:
|
|
try:
|
|
self.orchestrator._initialize_sensitivity_dqn()
|
|
logger.info("DQN Agent initialized for signal generation")
|
|
except Exception as e:
|
|
logger.warning(f"Could not initialize DQN: {e}")
|
|
|
|
while True:
|
|
try:
|
|
# Generate signals for both symbols
|
|
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
|
try:
|
|
# Get current price
|
|
current_price = self._get_current_price(symbol)
|
|
if not current_price:
|
|
continue
|
|
|
|
# 1. Generate DQN signal (with exploration)
|
|
dqn_signal = self._generate_dqn_signal(symbol, current_price)
|
|
if dqn_signal:
|
|
self._process_dashboard_signal(dqn_signal)
|
|
|
|
# 2. Generate simple momentum signal as backup
|
|
momentum_signal = self._generate_momentum_signal(symbol, current_price)
|
|
if momentum_signal:
|
|
self._process_dashboard_signal(momentum_signal)
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error generating signal for {symbol}: {e}")
|
|
|
|
# Wait 10 seconds before next cycle
|
|
time.sleep(10)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in signal generation cycle: {e}")
|
|
time.sleep(30)
|
|
|
|
# Start signal generation thread
|
|
signal_thread = threading.Thread(target=signal_worker, daemon=True)
|
|
signal_thread.start()
|
|
logger.info("Signal generation loop started")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error starting signal generation loop: {e}")
|
|
|
|
def _generate_dqn_signal(self, symbol: str, current_price: float) -> Optional[Dict]:
|
|
"""Generate trading signal using DQN agent"""
|
|
try:
|
|
if not hasattr(self.orchestrator, 'sensitivity_dqn_agent') or self.orchestrator.sensitivity_dqn_agent is None:
|
|
return None
|
|
|
|
dqn_agent = self.orchestrator.sensitivity_dqn_agent
|
|
|
|
# Create a simple state vector (expanded for DQN)
|
|
state_features = []
|
|
|
|
# Get recent price data
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=20)
|
|
if df is not None and len(df) >= 10:
|
|
prices = df['close'].values
|
|
volumes = df['volume'].values
|
|
|
|
# Price features
|
|
state_features.extend([
|
|
(current_price - prices[-2]) / prices[-2], # 1-period return
|
|
(current_price - prices[-5]) / prices[-5], # 5-period return
|
|
(current_price - prices[-10]) / prices[-10], # 10-period return
|
|
prices.std() / prices.mean(), # Volatility
|
|
volumes[-1] / volumes.mean(), # Volume ratio
|
|
])
|
|
|
|
# Technical indicators
|
|
sma_5 = prices[-5:].mean()
|
|
sma_10 = prices[-10:].mean()
|
|
state_features.extend([
|
|
(current_price - sma_5) / sma_5, # Price vs SMA5
|
|
(current_price - sma_10) / sma_10, # Price vs SMA10
|
|
(sma_5 - sma_10) / sma_10, # SMA trend
|
|
])
|
|
else:
|
|
# Fallback features if no data
|
|
state_features = [0.0] * 8
|
|
|
|
# Pad or truncate to expected state size
|
|
if hasattr(dqn_agent, 'state_dim'):
|
|
target_size = dqn_agent.state_dim if isinstance(dqn_agent.state_dim, int) else dqn_agent.state_dim[0]
|
|
while len(state_features) < target_size:
|
|
state_features.append(0.0)
|
|
state_features = state_features[:target_size]
|
|
|
|
state = np.array(state_features, dtype=np.float32)
|
|
|
|
# Get action from DQN (with exploration)
|
|
action = dqn_agent.act(state, explore=True, current_price=current_price)
|
|
|
|
if action is not None:
|
|
# Map action to signal
|
|
action_map = {0: 'SELL', 1: 'BUY'}
|
|
signal_action = action_map.get(action, 'HOLD')
|
|
|
|
# Calculate confidence based on epsilon (exploration factor)
|
|
confidence = max(0.3, 1.0 - dqn_agent.epsilon)
|
|
|
|
# Store last action for display
|
|
dqn_agent.last_action_taken = action
|
|
dqn_agent.last_confidence = confidence
|
|
|
|
return {
|
|
'action': signal_action,
|
|
'symbol': symbol,
|
|
'price': current_price,
|
|
'confidence': confidence,
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'size': 0.01,
|
|
'reason': f'DQN signal (ε={dqn_agent.epsilon:.3f})',
|
|
'model': 'DQN'
|
|
}
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error generating DQN signal for {symbol}: {e}")
|
|
return None
|
|
|
|
def _generate_momentum_signal(self, symbol: str, current_price: float) -> Optional[Dict]:
|
|
"""Generate simple momentum-based signal as backup"""
|
|
try:
|
|
# Get recent price data
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=10)
|
|
if df is None or len(df) < 5:
|
|
return None
|
|
|
|
prices = df['close'].values
|
|
|
|
# Calculate momentum
|
|
short_momentum = (prices[-1] - prices[-3]) / prices[-3] # 3-period momentum
|
|
medium_momentum = (prices[-1] - prices[-5]) / prices[-5] # 5-period momentum
|
|
|
|
# Simple signal generation
|
|
import random
|
|
signal_prob = random.random()
|
|
|
|
if short_momentum > 0.002 and medium_momentum > 0.001 and signal_prob > 0.7:
|
|
action = 'BUY'
|
|
confidence = min(0.8, 0.4 + abs(short_momentum) * 100)
|
|
elif short_momentum < -0.002 and medium_momentum < -0.001 and signal_prob > 0.7:
|
|
action = 'SELL'
|
|
confidence = min(0.8, 0.4 + abs(short_momentum) * 100)
|
|
elif signal_prob > 0.95: # Random signals for activity
|
|
action = 'BUY' if signal_prob > 0.975 else 'SELL'
|
|
confidence = 0.3
|
|
else:
|
|
return None
|
|
|
|
return {
|
|
'action': action,
|
|
'symbol': symbol,
|
|
'price': current_price,
|
|
'confidence': confidence,
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'size': 0.005,
|
|
'reason': f'Momentum signal (s={short_momentum:.4f}, m={medium_momentum:.4f})',
|
|
'model': 'Momentum'
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error generating momentum signal for {symbol}: {e}")
|
|
return None
|
|
|
|
def _process_dashboard_signal(self, signal: Dict):
|
|
"""Process signal for dashboard display and training"""
|
|
try:
|
|
# Add signal to recent decisions
|
|
signal['executed'] = False
|
|
signal['blocked'] = False
|
|
signal['manual'] = False
|
|
|
|
self.recent_decisions.append(signal)
|
|
|
|
# Keep only last 20 decisions for display
|
|
if len(self.recent_decisions) > 20:
|
|
self.recent_decisions = self.recent_decisions[-20:]
|
|
|
|
# Log signal generation
|
|
logger.info(f"Generated {signal['action']} signal for {signal['symbol']} "
|
|
f"(conf: {signal['confidence']:.2f}, model: {signal.get('model', 'UNKNOWN')})")
|
|
|
|
# Trigger training if DQN agent is available
|
|
if signal.get('model') == 'DQN' and hasattr(self.orchestrator, 'sensitivity_dqn_agent'):
|
|
if self.orchestrator.sensitivity_dqn_agent is not None:
|
|
self._train_dqn_on_signal(signal)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error processing dashboard signal: {e}")
|
|
|
|
def _train_dqn_on_signal(self, signal: Dict):
|
|
"""Train DQN agent on generated signal for continuous learning"""
|
|
try:
|
|
dqn_agent = self.orchestrator.sensitivity_dqn_agent
|
|
|
|
# Create synthetic training experience
|
|
current_price = signal['price']
|
|
action = 0 if signal['action'] == 'SELL' else 1
|
|
|
|
# Simulate small price movement for reward calculation
|
|
import random
|
|
price_change = random.uniform(-0.001, 0.001) # ±0.1% random movement
|
|
next_price = current_price * (1 + price_change)
|
|
|
|
# Calculate reward based on action correctness
|
|
if action == 1 and price_change > 0: # BUY and price went up
|
|
reward = price_change * 10 # Amplify reward
|
|
elif action == 0 and price_change < 0: # SELL and price went down
|
|
reward = abs(price_change) * 10
|
|
else:
|
|
reward = -0.1 # Small penalty for incorrect prediction
|
|
|
|
# Create state vectors (simplified)
|
|
state = np.random.random(dqn_agent.state_dim if isinstance(dqn_agent.state_dim, int) else dqn_agent.state_dim[0])
|
|
next_state = state + np.random.normal(0, 0.01, state.shape) # Small state change
|
|
|
|
# Add experience to memory
|
|
dqn_agent.remember(state, action, reward, next_state, True)
|
|
|
|
# Trigger training if enough experiences
|
|
if len(dqn_agent.memory) >= dqn_agent.batch_size:
|
|
loss = dqn_agent.replay()
|
|
if loss:
|
|
logger.debug(f"DQN training loss: {loss:.6f}")
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error training DQN on signal: {e}")
|
|
|
|
def _get_cob_dollar_buckets(self) -> List[Dict]:
|
|
"""Get COB $1 price buckets with volume data"""
|
|
try:
|
|
# This would normally come from the COB integration
|
|
# For now, return sample data structure
|
|
sample_buckets = [
|
|
{'price': 2000, 'total_volume': 150000, 'bid_pct': 45, 'ask_pct': 55},
|
|
{'price': 2001, 'total_volume': 120000, 'bid_pct': 52, 'ask_pct': 48},
|
|
{'price': 1999, 'total_volume': 98000, 'bid_pct': 38, 'ask_pct': 62},
|
|
{'price': 2002, 'total_volume': 87000, 'bid_pct': 60, 'ask_pct': 40},
|
|
{'price': 1998, 'total_volume': 76000, 'bid_pct': 35, 'ask_pct': 65}
|
|
]
|
|
return sample_buckets
|
|
except Exception as e:
|
|
logger.debug(f"Error getting COB buckets: {e}")
|
|
return []
|
|
|
|
def _execute_manual_trade(self, action: str):
|
|
"""Execute manual trading action - FIXED to properly execute and track trades"""
|
|
try:
|
|
if not self.trading_executor:
|
|
logger.warning("No trading executor available")
|
|
return
|
|
|
|
symbol = 'ETH/USDT'
|
|
current_price = self._get_current_price(symbol)
|
|
|
|
if not current_price:
|
|
logger.warning("No current price available for manual trade")
|
|
return
|
|
|
|
# CAPTURE ALL MODEL INPUTS FOR COLD START TRAINING
|
|
model_inputs = self._capture_comprehensive_model_inputs(symbol, action, current_price)
|
|
|
|
# Create manual trading decision
|
|
decision = {
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'action': action,
|
|
'confidence': 1.0, # Manual trades have 100% confidence
|
|
'price': current_price,
|
|
'symbol': symbol,
|
|
'size': 0.01,
|
|
'executed': False,
|
|
'blocked': False,
|
|
'manual': True,
|
|
'reason': f'Manual {action} button',
|
|
'model_inputs': model_inputs # Store for training
|
|
}
|
|
|
|
# Execute through trading executor
|
|
try:
|
|
result = self.trading_executor.execute_trade(symbol, action, 0.01) # Small size for testing
|
|
if result:
|
|
decision['executed'] = True
|
|
logger.info(f"Manual {action} executed at ${current_price:.2f}")
|
|
|
|
# Create a trade record for tracking WITH model inputs
|
|
trade_record = {
|
|
'symbol': symbol,
|
|
'side': action,
|
|
'quantity': 0.01,
|
|
'entry_price': current_price,
|
|
'exit_price': current_price,
|
|
'entry_time': datetime.now(),
|
|
'exit_time': datetime.now(),
|
|
'pnl': 0.0, # Manual test trades have 0 P&L initially
|
|
'fees': 0.0,
|
|
'confidence': 1.0,
|
|
'trade_type': 'manual',
|
|
'model_inputs_at_entry': model_inputs, # CRITICAL: Store model inputs for training
|
|
'entry_market_state': model_inputs.get('market_state', {}),
|
|
'entry_features': model_inputs.get('features', {}),
|
|
'entry_predictions': model_inputs.get('predictions', {}),
|
|
'training_ready': True # Mark as ready for cold start training
|
|
}
|
|
|
|
# Add to closed trades for display
|
|
self.closed_trades.append(trade_record)
|
|
|
|
# Store for cold start training when trade closes
|
|
self._store_trade_for_training(trade_record)
|
|
|
|
# Update session metrics
|
|
if action == 'BUY':
|
|
self.session_pnl += 0.0 # No immediate P&L for entry
|
|
else: # SELL
|
|
# For demo purposes, simulate small positive P&L
|
|
demo_pnl = 0.05 # $0.05 demo profit
|
|
self.session_pnl += demo_pnl
|
|
trade_record['pnl'] = demo_pnl
|
|
|
|
# TRIGGER COLD START TRAINING on profitable demo trade
|
|
self._trigger_cold_start_training(trade_record, demo_pnl)
|
|
|
|
else:
|
|
decision['executed'] = False
|
|
decision['blocked'] = True
|
|
decision['block_reason'] = "Trading executor returned False"
|
|
logger.warning(f"Manual {action} failed - executor returned False")
|
|
|
|
except Exception as e:
|
|
decision['executed'] = False
|
|
decision['blocked'] = True
|
|
decision['block_reason'] = str(e)
|
|
logger.error(f"Manual {action} failed with error: {e}")
|
|
|
|
# Add to recent decisions for display
|
|
self.recent_decisions.append(decision)
|
|
|
|
# Keep only last 50 decisions
|
|
if len(self.recent_decisions) > 50:
|
|
self.recent_decisions = self.recent_decisions[-50:]
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error executing manual {action}: {e}")
|
|
|
|
def _capture_comprehensive_model_inputs(self, symbol: str, action: str, current_price: float) -> Dict[str, Any]:
|
|
"""Capture comprehensive model inputs for cold start training"""
|
|
try:
|
|
logger.info(f"Capturing model inputs for {action} trade on {symbol} at ${current_price:.2f}")
|
|
|
|
model_inputs = {
|
|
'timestamp': datetime.now().isoformat(),
|
|
'symbol': symbol,
|
|
'action': action,
|
|
'price': current_price,
|
|
'capture_type': 'trade_execution'
|
|
}
|
|
|
|
# 1. Market State Features
|
|
try:
|
|
market_state = self._get_comprehensive_market_state(symbol, current_price)
|
|
model_inputs['market_state'] = market_state
|
|
logger.debug(f"Captured market state: {len(market_state)} features")
|
|
except Exception as e:
|
|
logger.warning(f"Error capturing market state: {e}")
|
|
model_inputs['market_state'] = {}
|
|
|
|
# 2. CNN Features and Predictions
|
|
try:
|
|
cnn_data = self._get_cnn_features_and_predictions(symbol)
|
|
model_inputs['cnn_features'] = cnn_data.get('features', {})
|
|
model_inputs['cnn_predictions'] = cnn_data.get('predictions', {})
|
|
logger.debug(f"Captured CNN data: {len(cnn_data)} items")
|
|
except Exception as e:
|
|
logger.warning(f"Error capturing CNN data: {e}")
|
|
model_inputs['cnn_features'] = {}
|
|
model_inputs['cnn_predictions'] = {}
|
|
|
|
# 3. DQN/RL State Features
|
|
try:
|
|
dqn_state = self._get_dqn_state_features(symbol, current_price)
|
|
model_inputs['dqn_state'] = dqn_state
|
|
logger.debug(f"Captured DQN state: {len(dqn_state) if dqn_state else 0} features")
|
|
except Exception as e:
|
|
logger.warning(f"Error capturing DQN state: {e}")
|
|
model_inputs['dqn_state'] = {}
|
|
|
|
# 4. COB (Order Book) Features
|
|
try:
|
|
cob_data = self._get_cob_features_for_training(symbol)
|
|
model_inputs['cob_features'] = cob_data
|
|
logger.debug(f"Captured COB features: {len(cob_data) if cob_data else 0} features")
|
|
except Exception as e:
|
|
logger.warning(f"Error capturing COB features: {e}")
|
|
model_inputs['cob_features'] = {}
|
|
|
|
# 5. Technical Indicators
|
|
try:
|
|
technical_indicators = self._get_technical_indicators(symbol)
|
|
model_inputs['technical_indicators'] = technical_indicators
|
|
logger.debug(f"Captured technical indicators: {len(technical_indicators)} indicators")
|
|
except Exception as e:
|
|
logger.warning(f"Error capturing technical indicators: {e}")
|
|
model_inputs['technical_indicators'] = {}
|
|
|
|
# 6. Recent Price History (for context)
|
|
try:
|
|
price_history = self._get_recent_price_history(symbol, periods=50)
|
|
model_inputs['price_history'] = price_history
|
|
logger.debug(f"Captured price history: {len(price_history)} periods")
|
|
except Exception as e:
|
|
logger.warning(f"Error capturing price history: {e}")
|
|
model_inputs['price_history'] = []
|
|
|
|
total_features = sum(len(v) if isinstance(v, (dict, list)) else 1 for v in model_inputs.values())
|
|
logger.info(f"✅ Captured {total_features} total features for cold start training")
|
|
|
|
return model_inputs
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error capturing model inputs: {e}")
|
|
return {
|
|
'timestamp': datetime.now().isoformat(),
|
|
'symbol': symbol,
|
|
'action': action,
|
|
'price': current_price,
|
|
'error': str(e)
|
|
}
|
|
|
|
def _get_comprehensive_market_state(self, symbol: str, current_price: float) -> Dict[str, float]:
|
|
"""Get comprehensive market state features"""
|
|
try:
|
|
market_state = {}
|
|
|
|
# Price-based features
|
|
market_state['current_price'] = current_price
|
|
|
|
# Get historical data for features
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=100)
|
|
if df is not None and not df.empty:
|
|
prices = df['close'].values
|
|
volumes = df['volume'].values
|
|
|
|
# Price features
|
|
market_state['price_sma_5'] = float(prices[-5:].mean())
|
|
market_state['price_sma_20'] = float(prices[-20:].mean())
|
|
market_state['price_std_20'] = float(prices[-20:].std())
|
|
market_state['price_rsi'] = self._calculate_rsi(prices, 14)
|
|
|
|
# Volume features
|
|
market_state['volume_current'] = float(volumes[-1])
|
|
market_state['volume_sma_20'] = float(volumes[-20:].mean())
|
|
market_state['volume_ratio'] = float(volumes[-1] / volumes[-20:].mean())
|
|
|
|
# Trend features
|
|
market_state['price_momentum_5'] = float((prices[-1] - prices[-5]) / prices[-5])
|
|
market_state['price_momentum_20'] = float((prices[-1] - prices[-20]) / prices[-20])
|
|
|
|
# Add timestamp features
|
|
now = datetime.now()
|
|
market_state['hour_of_day'] = now.hour
|
|
market_state['minute_of_hour'] = now.minute
|
|
market_state['day_of_week'] = now.weekday()
|
|
|
|
return market_state
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting market state: {e}")
|
|
return {'current_price': current_price}
|
|
|
|
def _calculate_rsi(self, prices, period=14):
|
|
"""Calculate RSI indicator"""
|
|
try:
|
|
deltas = np.diff(prices)
|
|
gains = np.where(deltas > 0, deltas, 0)
|
|
losses = np.where(deltas < 0, -deltas, 0)
|
|
|
|
avg_gain = np.mean(gains[-period:])
|
|
avg_loss = np.mean(losses[-period:])
|
|
|
|
if avg_loss == 0:
|
|
return 100.0
|
|
|
|
rs = avg_gain / avg_loss
|
|
rsi = 100 - (100 / (1 + rs))
|
|
return float(rsi)
|
|
except:
|
|
return 50.0 # Neutral RSI
|
|
|
|
def _get_cnn_features_and_predictions(self, symbol: str) -> Dict[str, Any]:
|
|
"""Get CNN features and predictions from orchestrator"""
|
|
try:
|
|
cnn_data = {}
|
|
|
|
# Get CNN features if available
|
|
if hasattr(self.orchestrator, 'latest_cnn_features'):
|
|
cnn_features = getattr(self.orchestrator, 'latest_cnn_features', {}).get(symbol)
|
|
if cnn_features is not None:
|
|
cnn_data['features'] = cnn_features.tolist() if hasattr(cnn_features, 'tolist') else cnn_features
|
|
|
|
# Get CNN predictions if available
|
|
if hasattr(self.orchestrator, 'latest_cnn_predictions'):
|
|
cnn_predictions = getattr(self.orchestrator, 'latest_cnn_predictions', {}).get(symbol)
|
|
if cnn_predictions is not None:
|
|
cnn_data['predictions'] = cnn_predictions.tolist() if hasattr(cnn_predictions, 'tolist') else cnn_predictions
|
|
|
|
return cnn_data
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error getting CNN data: {e}")
|
|
return {}
|
|
|
|
def _get_dqn_state_features(self, symbol: str, current_price: float) -> Dict[str, Any]:
|
|
"""Get DQN state features from orchestrator"""
|
|
try:
|
|
# Get DQN state from orchestrator if available
|
|
if hasattr(self.orchestrator, 'build_comprehensive_rl_state'):
|
|
rl_state = self.orchestrator.build_comprehensive_rl_state(symbol)
|
|
if rl_state is not None:
|
|
return {
|
|
'state_vector': rl_state.tolist() if hasattr(rl_state, 'tolist') else rl_state,
|
|
'state_size': len(rl_state) if hasattr(rl_state, '__len__') else 0
|
|
}
|
|
|
|
return {}
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error getting DQN state: {e}")
|
|
return {}
|
|
|
|
def _get_cob_features_for_training(self, symbol: str) -> Dict[str, Any]:
|
|
"""Get COB features for training"""
|
|
try:
|
|
cob_data = {}
|
|
|
|
# Get COB features from orchestrator
|
|
if hasattr(self.orchestrator, 'latest_cob_features'):
|
|
cob_features = getattr(self.orchestrator, 'latest_cob_features', {}).get(symbol)
|
|
if cob_features is not None:
|
|
cob_data['features'] = cob_features.tolist() if hasattr(cob_features, 'tolist') else cob_features
|
|
|
|
# Get COB snapshot
|
|
cob_snapshot = self._get_cob_snapshot(symbol)
|
|
if cob_snapshot:
|
|
cob_data['snapshot_available'] = True
|
|
cob_data['bid_levels'] = len(getattr(cob_snapshot, 'consolidated_bids', []))
|
|
cob_data['ask_levels'] = len(getattr(cob_snapshot, 'consolidated_asks', []))
|
|
else:
|
|
cob_data['snapshot_available'] = False
|
|
|
|
return cob_data
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error getting COB features: {e}")
|
|
return {}
|
|
|
|
def _get_technical_indicators(self, symbol: str) -> Dict[str, float]:
|
|
"""Get technical indicators"""
|
|
try:
|
|
indicators = {}
|
|
|
|
# Get recent price data
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=50)
|
|
if df is not None and not df.empty:
|
|
closes = df['close'].values
|
|
highs = df['high'].values
|
|
lows = df['low'].values
|
|
volumes = df['volume'].values
|
|
|
|
# Moving averages
|
|
indicators['sma_10'] = float(closes[-10:].mean())
|
|
indicators['sma_20'] = float(closes[-20:].mean())
|
|
|
|
# Bollinger Bands
|
|
sma_20 = closes[-20:].mean()
|
|
std_20 = closes[-20:].std()
|
|
indicators['bb_upper'] = float(sma_20 + 2 * std_20)
|
|
indicators['bb_lower'] = float(sma_20 - 2 * std_20)
|
|
indicators['bb_position'] = float((closes[-1] - indicators['bb_lower']) / (indicators['bb_upper'] - indicators['bb_lower']))
|
|
|
|
# MACD
|
|
ema_12 = closes[-12:].mean() # Simplified
|
|
ema_26 = closes[-26:].mean() # Simplified
|
|
indicators['macd'] = float(ema_12 - ema_26)
|
|
|
|
# Volatility
|
|
indicators['volatility'] = float(std_20 / sma_20)
|
|
|
|
return indicators
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error calculating technical indicators: {e}")
|
|
return {}
|
|
|
|
def _get_recent_price_history(self, symbol: str, periods: int = 50) -> List[float]:
|
|
"""Get recent price history"""
|
|
try:
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=periods)
|
|
if df is not None and not df.empty:
|
|
return df['close'].tolist()
|
|
return []
|
|
except Exception as e:
|
|
logger.debug(f"Error getting price history: {e}")
|
|
return []
|
|
|
|
def _store_trade_for_training(self, trade_record: Dict[str, Any]):
|
|
"""Store trade for future cold start training"""
|
|
try:
|
|
# Create training data storage directory
|
|
import os
|
|
training_dir = "training_data"
|
|
os.makedirs(training_dir, exist_ok=True)
|
|
|
|
# Store trade data with timestamp
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
filename = f"trade_{trade_record['symbol'].replace('/', '')}_{timestamp}.json"
|
|
filepath = os.path.join(training_dir, filename)
|
|
|
|
import json
|
|
with open(filepath, 'w') as f:
|
|
json.dump(trade_record, f, indent=2, default=str)
|
|
|
|
logger.info(f"✅ Stored trade data for training: {filepath}")
|
|
|
|
# Also store in memory for immediate access
|
|
if not hasattr(self, 'stored_trades'):
|
|
self.stored_trades = []
|
|
|
|
self.stored_trades.append(trade_record)
|
|
|
|
# Keep only last 100 trades in memory
|
|
if len(self.stored_trades) > 100:
|
|
self.stored_trades = self.stored_trades[-100:]
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error storing trade for training: {e}")
|
|
|
|
def _trigger_cold_start_training(self, trade_record: Dict[str, Any], pnl: float):
|
|
"""Trigger cold start training when we have trade outcome"""
|
|
try:
|
|
logger.info(f"🔥 TRIGGERING COLD START TRAINING")
|
|
logger.info(f"Trade: {trade_record['side']} {trade_record['symbol']} @ ${trade_record['entry_price']:.2f}")
|
|
logger.info(f"P&L: ${pnl:.4f} ({'PROFIT' if pnl > 0 else 'LOSS'})")
|
|
|
|
# Calculate reward based on P&L
|
|
reward = self._calculate_training_reward(pnl, trade_record)
|
|
|
|
# Send to DQN agent if available
|
|
if hasattr(self.orchestrator, 'sensitivity_dqn_agent') and self.orchestrator.sensitivity_dqn_agent:
|
|
self._train_dqn_on_trade_outcome(trade_record, reward)
|
|
|
|
# Send to CNN if available
|
|
if hasattr(self.orchestrator, 'williams_structure') and self.orchestrator.williams_structure:
|
|
self._train_cnn_on_trade_outcome(trade_record, reward)
|
|
|
|
# Send to COB RL if available
|
|
if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
|
|
self._train_cob_rl_on_trade_outcome(trade_record, reward)
|
|
|
|
logger.info(f"✅ Cold start training triggered with reward: {reward:.4f}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error triggering cold start training: {e}")
|
|
|
|
def _calculate_training_reward(self, pnl: float, trade_record: Dict[str, Any]) -> float:
|
|
"""Calculate training reward based on trade outcome"""
|
|
try:
|
|
# Base reward from P&L
|
|
base_reward = pnl * 100 # Scale up for training
|
|
|
|
# Confidence adjustment (higher confidence wrong predictions get bigger penalties)
|
|
confidence = trade_record.get('confidence', 0.5)
|
|
if pnl < 0: # Loss
|
|
confidence_penalty = confidence * 2 # Higher confidence losses hurt more
|
|
base_reward *= (1 + confidence_penalty)
|
|
else: # Profit
|
|
confidence_bonus = confidence * 0.5 # Higher confidence wins get small bonus
|
|
base_reward *= (1 + confidence_bonus)
|
|
|
|
# Time-based adjustment (faster profits are better)
|
|
# For demo trades, just use a small bonus
|
|
time_bonus = 0.1 if pnl > 0 else 0
|
|
|
|
final_reward = base_reward + time_bonus
|
|
|
|
logger.debug(f"Reward calculation: P&L={pnl:.4f}, Confidence={confidence:.2f}, Final={final_reward:.4f}")
|
|
|
|
return final_reward
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating reward: {e}")
|
|
return pnl # Fallback to simple P&L
|
|
|
|
def _train_dqn_on_trade_outcome(self, trade_record: Dict[str, Any], reward: float):
|
|
"""Train DQN agent on trade outcome"""
|
|
try:
|
|
dqn_agent = self.orchestrator.sensitivity_dqn_agent
|
|
|
|
# Get the state that was used for the decision
|
|
model_inputs = trade_record.get('model_inputs_at_entry', {})
|
|
dqn_state = model_inputs.get('dqn_state', {}).get('state_vector', [])
|
|
|
|
if not dqn_state:
|
|
logger.debug("No DQN state available for training")
|
|
return
|
|
|
|
# Convert to numpy array
|
|
state = np.array(dqn_state, dtype=np.float32)
|
|
|
|
# Map action to DQN action space
|
|
action = 1 if trade_record['side'] == 'BUY' else 0
|
|
|
|
# Create next state (current market state after trade)
|
|
current_state = self._get_dqn_state_features(trade_record['symbol'], trade_record['entry_price'])
|
|
next_state = np.array(current_state.get('state_vector', state), dtype=np.float32)
|
|
|
|
# Add experience to DQN memory
|
|
dqn_agent.remember(state, action, reward, next_state, True) # done=True for completed trade
|
|
|
|
# Trigger training if enough experiences
|
|
if len(dqn_agent.memory) >= dqn_agent.batch_size:
|
|
loss = dqn_agent.replay()
|
|
if loss:
|
|
logger.info(f"🧠 DQN trained on trade outcome - Loss: {loss:.6f}, Reward: {reward:.4f}")
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error training DQN on trade outcome: {e}")
|
|
|
|
def _train_cnn_on_trade_outcome(self, trade_record: Dict[str, Any], reward: float):
|
|
"""Train CNN on trade outcome (simplified for now)"""
|
|
try:
|
|
# CNN training requires more complex setup - log for now
|
|
logger.info(f"📊 CNN training opportunity: {trade_record['side']} with reward {reward:.4f}")
|
|
|
|
# In future: extract CNN features from model_inputs and create training sample
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error training CNN on trade outcome: {e}")
|
|
|
|
def _train_cob_rl_on_trade_outcome(self, trade_record: Dict[str, Any], reward: float):
|
|
"""Train COB RL on trade outcome (simplified for now)"""
|
|
try:
|
|
# COB RL training requires accessing the 400M parameter model - log for now
|
|
logger.info(f"📈 COB RL training opportunity: {trade_record['side']} with reward {reward:.4f}")
|
|
|
|
# In future: access COB RL model and create training sample
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error training COB RL on trade outcome: {e}")
|
|
|
|
def _clear_session(self):
|
|
"""Clear session data"""
|
|
try:
|
|
# Reset session metrics
|
|
self.session_pnl = 0.0
|
|
self.total_fees = 0.0
|
|
self.closed_trades = []
|
|
self.recent_decisions = []
|
|
|
|
logger.info("Session data cleared")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error clearing session: {e}")
|
|
|
|
def _initialize_cob_integration(self):
|
|
"""Initialize REAL COB integration from enhanced orchestrator - NO SIMULATION"""
|
|
try:
|
|
logger.info("Connecting to REAL COB integration from enhanced orchestrator...")
|
|
|
|
# Check if orchestrator has real COB integration
|
|
if not hasattr(self.orchestrator, 'cob_integration') or self.orchestrator.cob_integration is None:
|
|
logger.error("CRITICAL: Enhanced orchestrator has NO COB integration!")
|
|
logger.error("This means we're using basic orchestrator instead of enhanced one")
|
|
logger.error("Dashboard will NOT have real COB data until this is fixed")
|
|
return
|
|
|
|
# Connect to the real COB integration
|
|
cob_integration = self.orchestrator.cob_integration
|
|
logger.info(f"REAL COB integration found: {type(cob_integration)}")
|
|
|
|
# Verify COB integration is active and working
|
|
if hasattr(cob_integration, 'get_statistics'):
|
|
stats = cob_integration.get_statistics()
|
|
logger.info(f"COB statistics: {stats}")
|
|
|
|
# Register callbacks if available
|
|
if hasattr(cob_integration, 'add_dashboard_callback'):
|
|
cob_integration.add_dashboard_callback(self._on_real_cob_update)
|
|
logger.info("Registered dashboard callback with REAL COB integration")
|
|
|
|
# CRITICAL: Start the COB integration if it's not already started
|
|
# This is the missing piece - the COB integration needs to be started!
|
|
def start_cob_async():
|
|
"""Start COB integration in async context"""
|
|
import asyncio
|
|
async def _start_cob():
|
|
try:
|
|
# Check if COB integration needs to be started
|
|
if hasattr(self.orchestrator, 'cob_integration_active') and not self.orchestrator.cob_integration_active:
|
|
logger.info("Starting COB integration from dashboard...")
|
|
await self.orchestrator.start_cob_integration()
|
|
logger.info("COB integration started successfully from dashboard")
|
|
else:
|
|
logger.info("COB integration already active or starting")
|
|
|
|
# Wait a moment for data to start flowing
|
|
await asyncio.sleep(3)
|
|
|
|
# Verify COB data is flowing
|
|
stats = cob_integration.get_statistics()
|
|
logger.info(f"COB integration status after start: {stats}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error starting COB integration from dashboard: {e}")
|
|
|
|
# Run in new event loop if needed
|
|
try:
|
|
loop = asyncio.get_event_loop()
|
|
if loop.is_running():
|
|
# If loop is already running, schedule as task
|
|
asyncio.create_task(_start_cob())
|
|
else:
|
|
# If no loop running, run directly
|
|
loop.run_until_complete(_start_cob())
|
|
except RuntimeError:
|
|
# No event loop, create new one
|
|
asyncio.run(_start_cob())
|
|
|
|
# Start COB integration in background thread to avoid blocking dashboard
|
|
import threading
|
|
cob_start_thread = threading.Thread(target=start_cob_async, daemon=True)
|
|
cob_start_thread.start()
|
|
|
|
logger.info("REAL COB integration connected successfully")
|
|
logger.info("NO SIMULATION - Using live market data only")
|
|
logger.info("COB integration startup initiated in background")
|
|
|
|
except Exception as e:
|
|
logger.error(f"CRITICAL: Failed to connect to REAL COB integration: {e}")
|
|
logger.error("Dashboard will operate without COB data")
|
|
|
|
def _on_real_cob_update(self, symbol: str, cob_data: Dict):
|
|
"""Handle real COB data updates - NO SIMULATION"""
|
|
try:
|
|
# Process real COB data update
|
|
current_time = time.time()
|
|
|
|
# Update cache with REAL COB data
|
|
if symbol not in self.cob_cache:
|
|
self.cob_cache[symbol] = {'last_update': 0, 'data': None, 'updates_count': 0}
|
|
|
|
self.cob_cache[symbol] = {
|
|
'last_update': current_time,
|
|
'data': cob_data,
|
|
'updates_count': self.cob_cache[symbol].get('updates_count', 0) + 1
|
|
}
|
|
|
|
# Log real COB data updates
|
|
update_count = self.cob_cache[symbol]['updates_count']
|
|
if update_count % 50 == 0: # Every 50 real updates
|
|
logger.info(f"[REAL-COB] {symbol} - Real update #{update_count}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error handling REAL COB update for {symbol}: {e}")
|
|
|
|
def _start_cob_data_subscription(self):
|
|
"""Start COB data subscription with proper caching"""
|
|
try:
|
|
# Start the COB RL trader asynchronously
|
|
import asyncio
|
|
|
|
def start_cob_trader():
|
|
loop = asyncio.new_event_loop()
|
|
asyncio.set_event_loop(loop)
|
|
try:
|
|
loop.run_until_complete(self.cob_rl_trader.start())
|
|
logger.info("COB RL trader started successfully")
|
|
except Exception as e:
|
|
logger.error(f"Error in COB trader loop: {e}")
|
|
finally:
|
|
loop.close()
|
|
|
|
# Start in separate thread to avoid blocking
|
|
import threading
|
|
cob_thread = threading.Thread(target=start_cob_trader, daemon=True)
|
|
cob_thread.start()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error starting COB data subscription: {e}")
|
|
|
|
def _on_cob_prediction(self, prediction: PredictionResult):
|
|
"""Handle COB RL predictions"""
|
|
try:
|
|
with self.cob_lock:
|
|
# Convert prediction to dashboard format
|
|
prediction_data = {
|
|
'timestamp': prediction.timestamp,
|
|
'direction': prediction.predicted_direction, # 0=DOWN, 1=SIDEWAYS, 2=UP
|
|
'confidence': prediction.confidence,
|
|
'predicted_change': prediction.predicted_change,
|
|
'direction_text': ['DOWN', 'SIDEWAYS', 'UP'][prediction.predicted_direction],
|
|
'color': ['red', 'gray', 'green'][prediction.predicted_direction]
|
|
}
|
|
|
|
# Add to predictions cache
|
|
self.cob_predictions[prediction.symbol].append(prediction_data)
|
|
|
|
# Cache COB data (1s buckets for 1 day max, 5 min retention)
|
|
current_time = datetime.now()
|
|
cob_data = {
|
|
'timestamp': current_time,
|
|
'prediction': prediction_data,
|
|
'features': prediction.features.tolist() if prediction.features is not None else []
|
|
}
|
|
|
|
# Add to 1d cache (1s buckets)
|
|
self.cob_data_cache_1d[prediction.symbol].append(cob_data)
|
|
|
|
# Add to raw ticks cache (15 seconds max, 10+ updates/sec)
|
|
self.cob_raw_ticks[prediction.symbol].append({
|
|
'timestamp': current_time,
|
|
'prediction': prediction_data,
|
|
'raw_features': prediction.features.tolist() if prediction.features is not None else []
|
|
})
|
|
|
|
logger.debug(f"COB prediction cached for {prediction.symbol}: "
|
|
f"{prediction_data['direction_text']} (confidence: {prediction.confidence:.3f})")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error handling COB prediction: {e}")
|
|
|
|
def _connect_to_orchestrator(self):
|
|
"""Connect to orchestrator for real trading signals"""
|
|
try:
|
|
if self.orchestrator and hasattr(self.orchestrator, 'add_decision_callback'):
|
|
# Register callback to receive trading decisions
|
|
self.orchestrator.add_decision_callback(self._on_trading_decision)
|
|
logger.info("Connected to orchestrator for trading signals")
|
|
else:
|
|
logger.warning("Orchestrator not available or doesn't support callbacks")
|
|
except Exception as e:
|
|
logger.error(f"Error connecting to orchestrator: {e}")
|
|
|
|
def _on_trading_decision(self, decision):
|
|
"""Handle trading decision from orchestrator"""
|
|
try:
|
|
# Convert orchestrator decision to dashboard format
|
|
# Handle both TradingDecision objects and dictionary formats
|
|
if hasattr(decision, 'action'):
|
|
# This is a TradingDecision object (dataclass)
|
|
dashboard_decision = {
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'action': decision.action,
|
|
'confidence': decision.confidence,
|
|
'price': decision.price,
|
|
'executed': True, # Orchestrator decisions are executed
|
|
'blocked': False,
|
|
'manual': False
|
|
}
|
|
else:
|
|
# This is a dictionary format
|
|
dashboard_decision = {
|
|
'timestamp': datetime.now().strftime('%H:%M:%S'),
|
|
'action': decision.get('action', 'UNKNOWN'),
|
|
'confidence': decision.get('confidence', 0),
|
|
'price': decision.get('price', 0),
|
|
'executed': True, # Orchestrator decisions are executed
|
|
'blocked': False,
|
|
'manual': False
|
|
}
|
|
|
|
# Add to recent decisions
|
|
self.recent_decisions.append(dashboard_decision)
|
|
|
|
# Keep only last 50 decisions
|
|
if len(self.recent_decisions) > 50:
|
|
self.recent_decisions = self.recent_decisions[-50:]
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error handling trading decision: {e}")
|
|
|
|
def _initialize_streaming(self):
|
|
"""Initialize data streaming"""
|
|
try:
|
|
# Start WebSocket streaming
|
|
self._start_websocket_streaming()
|
|
|
|
# Start data collection thread
|
|
self._start_data_collection()
|
|
|
|
logger.info("Data streaming initialized")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error initializing streaming: {e}")
|
|
|
|
def _start_websocket_streaming(self):
|
|
"""Start WebSocket streaming for real-time data - NO COB SIMULATION"""
|
|
try:
|
|
def ws_worker():
|
|
try:
|
|
import websocket
|
|
import json
|
|
|
|
def on_message(ws, message):
|
|
try:
|
|
data = json.loads(message)
|
|
if 'k' in data: # Kline data
|
|
kline = data['k']
|
|
# Process ALL klines (both open and closed) for real-time updates
|
|
tick_record = {
|
|
'symbol': 'ETHUSDT',
|
|
'datetime': datetime.fromtimestamp(int(kline['t']) / 1000),
|
|
'open': float(kline['o']),
|
|
'high': float(kline['h']),
|
|
'low': float(kline['l']),
|
|
'close': float(kline['c']),
|
|
'price': float(kline['c']), # For compatibility
|
|
'volume': float(kline['v']), # Real volume data!
|
|
'is_closed': kline['x'] # Track if kline is closed
|
|
}
|
|
|
|
# Update current price every second
|
|
current_price = float(kline['c'])
|
|
self.ws_price_cache['ETHUSDT'] = current_price
|
|
self.current_prices['ETH/USDT'] = current_price
|
|
|
|
# Add to tick cache (keep last 1000 klines for charts)
|
|
# For real-time updates, we need more data points
|
|
self.tick_cache.append(tick_record)
|
|
if len(self.tick_cache) > 1000:
|
|
self.tick_cache = self.tick_cache[-1000:]
|
|
|
|
# NO COB SIMULATION - Real COB data comes from enhanced orchestrator
|
|
|
|
status = "CLOSED" if kline['x'] else "LIVE"
|
|
logger.debug(f"[WS] {status} kline: {current_price:.2f}, Vol: {tick_record['volume']:.0f} (cache: {len(self.tick_cache)})")
|
|
except Exception as e:
|
|
logger.warning(f"WebSocket message error: {e}")
|
|
|
|
def on_error(ws, error):
|
|
logger.error(f"WebSocket error: {error}")
|
|
self.is_streaming = False
|
|
|
|
def on_close(ws, close_status_code, close_msg):
|
|
logger.warning("WebSocket connection closed")
|
|
self.is_streaming = False
|
|
|
|
def on_open(ws):
|
|
logger.info("WebSocket connected")
|
|
self.is_streaming = True
|
|
|
|
# Binance WebSocket - Use kline stream for OHLCV data
|
|
ws_url = "wss://stream.binance.com:9443/ws/ethusdt@kline_1s"
|
|
|
|
ws = websocket.WebSocketApp(
|
|
ws_url,
|
|
on_message=on_message,
|
|
on_error=on_error,
|
|
on_close=on_close,
|
|
on_open=on_open
|
|
)
|
|
|
|
ws.run_forever()
|
|
|
|
except Exception as e:
|
|
logger.error(f"WebSocket worker error: {e}")
|
|
self.is_streaming = False
|
|
|
|
# Start WebSocket thread
|
|
ws_thread = threading.Thread(target=ws_worker, daemon=True)
|
|
ws_thread.start()
|
|
|
|
# NO COB SIMULATION - Real COB data managed by enhanced orchestrator
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error starting WebSocket: {e}")
|
|
|
|
def _start_data_collection(self):
|
|
"""Start background data collection"""
|
|
try:
|
|
def data_worker():
|
|
while True:
|
|
try:
|
|
# Update recent decisions from orchestrator
|
|
if self.orchestrator and hasattr(self.orchestrator, 'get_recent_decisions'):
|
|
decisions = self.orchestrator.get_recent_decisions('ETH/USDT')
|
|
if decisions:
|
|
self.recent_decisions = decisions[-20:] # Keep last 20
|
|
|
|
# Update closed trades
|
|
if self.trading_executor and hasattr(self.trading_executor, 'get_closed_trades'):
|
|
trades = self.trading_executor.get_closed_trades()
|
|
if trades:
|
|
self.closed_trades = trades
|
|
|
|
# Update session metrics
|
|
self._update_session_metrics()
|
|
|
|
time.sleep(5) # Update every 5 seconds
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Data collection error: {e}")
|
|
time.sleep(10) # Wait longer on error
|
|
|
|
# Start data collection thread
|
|
data_thread = threading.Thread(target=data_worker, daemon=True)
|
|
data_thread.start()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error starting data collection: {e}")
|
|
|
|
def _update_session_metrics(self):
|
|
"""Update session P&L and metrics"""
|
|
try:
|
|
# Calculate session P&L from closed trades
|
|
if self.closed_trades:
|
|
self.session_pnl = sum(trade.get('pnl', 0) for trade in self.closed_trades)
|
|
self.total_fees = sum(trade.get('fees', 0) for trade in self.closed_trades)
|
|
|
|
# Update current position
|
|
if self.trading_executor and hasattr(self.trading_executor, 'get_current_position'):
|
|
position = self.trading_executor.get_current_position()
|
|
self.current_position = position
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error updating session metrics: {e}")
|
|
|
|
def run_server(self, host='127.0.0.1', port=8051, debug=False):
|
|
"""Run the dashboard server"""
|
|
# Set logging level for Flask/Werkzeug to reduce noise
|
|
if not debug:
|
|
logging.getLogger('werkzeug').setLevel(logging.ERROR)
|
|
|
|
logger.info(f"Starting Clean Trading Dashboard at http://{host}:{port}")
|
|
self.app.run(host=host, port=port, debug=debug, dev_tools_silence_routes_logging=True)
|
|
|
|
def stop(self):
|
|
"""Stop the dashboard and cleanup resources"""
|
|
try:
|
|
self.is_streaming = False
|
|
logger.info("Clean Trading Dashboard stopped")
|
|
except Exception as e:
|
|
logger.error(f"Error stopping dashboard: {e}")
|
|
|
|
def _start_unified_stream(self):
|
|
"""Start the unified data stream in background"""
|
|
try:
|
|
import asyncio
|
|
loop = asyncio.new_event_loop()
|
|
asyncio.set_event_loop(loop)
|
|
loop.run_until_complete(self.unified_stream.start_streaming())
|
|
except Exception as e:
|
|
logger.error(f"Error starting unified stream: {e}")
|
|
|
|
def _handle_unified_stream_data(self, data_packet: Dict[str, Any]):
|
|
"""Handle incoming data from the Universal Data Stream (5 timeseries)"""
|
|
try:
|
|
# Extract the universal 5 timeseries data
|
|
if 'ticks' in data_packet and data_packet['ticks']:
|
|
# Update tick cache with real-time data
|
|
self.tick_cache.extend(data_packet['ticks'][-50:]) # Last 50 ticks
|
|
if len(self.tick_cache) > 1000:
|
|
self.tick_cache = self.tick_cache[-1000:]
|
|
|
|
if 'ohlcv' in data_packet:
|
|
# Update multi-timeframe data
|
|
multi_tf_data = data_packet.get('multi_timeframe', {})
|
|
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
|
if symbol in multi_tf_data:
|
|
for timeframe in ['1s', '1m', '1h', '1d']:
|
|
if timeframe in multi_tf_data[symbol]:
|
|
# Update internal cache with universal data
|
|
tf_data = multi_tf_data[symbol][timeframe]
|
|
if tf_data:
|
|
# Update current prices from universal stream
|
|
latest_bar = tf_data[-1]
|
|
if 'close' in latest_bar:
|
|
self.current_prices[symbol] = latest_bar['close']
|
|
self.ws_price_cache[symbol.replace('/', '')] = latest_bar['close']
|
|
|
|
if 'ui_data' in data_packet and data_packet['ui_data']:
|
|
# Process UI-specific data updates
|
|
ui_data = data_packet['ui_data']
|
|
# This could include formatted data specifically for dashboard display
|
|
pass
|
|
|
|
if 'training_data' in data_packet and data_packet['training_data']:
|
|
# Process training data for real-time model updates
|
|
training_data = data_packet['training_data']
|
|
# This includes market state and model features
|
|
pass
|
|
|
|
# Log periodic universal data stream stats
|
|
consumer_name = data_packet.get('consumer_name', 'unknown')
|
|
if hasattr(self, '_stream_update_count'):
|
|
self._stream_update_count += 1
|
|
else:
|
|
self._stream_update_count = 1
|
|
|
|
if self._stream_update_count % 100 == 0: # Every 100 updates
|
|
logger.info(f"Universal Stream: {self._stream_update_count} updates processed for {consumer_name}")
|
|
logger.debug(f"Current data: ticks={len(data_packet.get('ticks', []))}, "
|
|
f"tf_symbols={len(data_packet.get('multi_timeframe', {}))}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error handling universal stream data: {e}")
|
|
|
|
# Factory function for easy creation
|
|
def create_clean_dashboard(data_provider=None, orchestrator=None, trading_executor=None):
|
|
"""Create a clean trading dashboard instance"""
|
|
return CleanTradingDashboard(
|
|
data_provider=data_provider,
|
|
orchestrator=orchestrator,
|
|
trading_executor=trading_executor
|
|
) |