1220 lines
55 KiB
Python
1220 lines
55 KiB
Python
"""
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Template-based Trading Dashboard
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Uses MVC architecture with HTML templates and data models
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"""
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import logging
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import sys
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import os
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from typing import Optional, Any, Dict, List, Deque
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from datetime import datetime, timedelta
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import pandas as pd
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import pytz
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import time
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import threading
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from collections import deque
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from dataclasses import asdict
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import dash
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from dash import dcc, html, Input, Output, State, callback_context
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import plotly.graph_objects as go
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import plotly.express as px
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from core.data_provider import DataProvider
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from core.orchestrator import TradingOrchestrator
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from core.trading_executor import TradingExecutor
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from core.config import get_config
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from core.universal_data_adapter import UniversalDataAdapter, UniversalDataStream
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from web.dashboard_model import DashboardModel, DashboardDataBuilder, create_sample_dashboard_data
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from web.template_renderer import DashboardTemplateRenderer
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from web.component_manager import DashboardComponentManager
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from web.layout_manager import DashboardLayoutManager
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from utils.checkpoint_manager import save_checkpoint, load_best_checkpoint
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from NN.models.advanced_transformer_trading import create_trading_transformer, TradingTransformerConfig
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# Configure logging
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logger = logging.getLogger(__name__)
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class TemplatedTradingDashboard:
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"""Template-based trading dashboard with MVC architecture"""
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def __init__(self, data_provider: Optional[DataProvider] = None,
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orchestrator: Optional[TradingOrchestrator] = None,
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trading_executor: Optional[TradingExecutor] = None):
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"""Initialize the templated dashboard"""
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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 template renderer
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self.renderer = DashboardTemplateRenderer()
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# Initialize unified orchestrator with full ML capabilities
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if orchestrator is None:
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self.orchestrator = TradingOrchestrator(
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data_provider=self.data_provider,
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enhanced_rl_training=True,
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model_registry={}
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)
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logger.info("TEMPLATED DASHBOARD: Using unified Trading Orchestrator with full ML capabilities")
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else:
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self.orchestrator = orchestrator
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# Initialize enhanced training system for predictions
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self.training_system = None
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self._initialize_enhanced_training_system()
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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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self.universal_adapter = UniversalDataAdapter(self.data_provider)
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# Data access now through orchestrator instead of complex stream management
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logger.debug("Universal Data Adapter initialized - accessing data through orchestrator")
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logger.info(f"TEMPLATED DASHBOARD: Universal Data Stream initialized with consumer ID: {self.stream_consumer_id}")
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logger.info("TEMPLATED DASHBOARD: Subscribed to Universal 5 Timeseries: ETH(ticks,1m,1h,1d) + BTC(ticks)")
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# Dashboard state
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self.recent_decisions: list = []
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self.closed_trades: list = []
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self.current_prices: dict = {}
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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: Optional[float] = 0.0
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self.session_trades: list = []
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# Model control toggles - separate inference and training
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self.dqn_inference_enabled = True # Default: enabled
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self.dqn_training_enabled = True # Default: enabled
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self.cnn_inference_enabled = True
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self.cnn_training_enabled = True
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# Leverage management - adjustable x1 to x100
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self.current_leverage = 50 # Default x50 leverage
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self.min_leverage = 1
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self.max_leverage = 100
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self.pending_trade_case_id = None # For tracking opening trades until closure
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# WebSocket streaming
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self.ws_price_cache: dict = {}
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self.is_streaming = False
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self.tick_cache: list = []
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# COB data cache - enhanced with price buckets and memory system
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self.cob_cache: dict = {
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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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self.latest_cob_data: dict = {} # Cache for COB integration data
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self.cob_predictions: dict = {} # Cache for COB predictions (both ETH and BTC for display)
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# COB High-frequency data handling (50-100 updates/sec)
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self.cob_data_buffer: dict = {} # Buffer for high-freq data
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self.cob_memory: dict = {} # Memory system like GPT - keeps last N snapshots
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self.cob_price_buckets: dict = {} # Price bucket cache
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self.cob_update_count = 0
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self.last_cob_broadcast: Dict[str, Optional[float]] = {'ETH/USDT': None, 'BTC/USDT': None} # Rate limiting for UI updates, updated type
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self.cob_data_history: Dict[str, Deque[Any]] = {
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'ETH/USDT': deque(maxlen=61), # Store ~60 seconds of 1s snapshots
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'BTC/USDT': deque(maxlen=61)
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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.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 COB integration with high-frequency data handling
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self._initialize_cob_integration()
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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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# Start training sessions if models are showing FRESH status
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threading.Thread(target=self._delayed_training_check, daemon=True).start()
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logger.info("TEMPLATED DASHBOARD: Initialized with HIGH-FREQUENCY COB integration and signal generation")
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def _setup_layout(self):
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"""Setup the dashboard layout using templates"""
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# Create initial dashboard data
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dashboard_data = self._build_dashboard_data()
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# Render layout using template
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layout = self.renderer.render_dashboard(dashboard_data)
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# Custom CSS will be handled via external stylesheets
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self.app.layout = layout
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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_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('trade-count', 'children'),
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Output('portfolio-value', '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 main 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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# Calculate portfolio value
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portfolio_value = 10000.0 + self.session_pnl # Base + PnL
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# Get MEXC status
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mexc_status = "Connected" if self.trading_executor else "Disconnected"
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return (
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f"${current_price:.4f}" if current_price else "N/A",
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f"${self.session_pnl:.2f}",
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f"{self.current_position:.4f}",
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str(len(self.session_trades)),
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f"${portfolio_value:.2f}",
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mexc_status
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)
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except Exception as e:
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logger.error(f"Error updating metrics: {e}")
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return "N/A", "N/A", "N/A", "N/A", "N/A", "Error"
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@self.app.callback(
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Output('price-chart', 'figure'),
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[Input('interval-component', 'n_intervals')]
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)
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def update_price_chart(n):
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"""Update price chart"""
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try:
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return self._create_price_chart("ETH/USDT")
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except Exception as e:
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logger.error(f"Error updating chart: {e}")
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return go.Figure()
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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):
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"""Update recent AI decisions"""
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try:
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decisions = self._get_recent_decisions()
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return self._render_decisions(decisions)
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except Exception as e:
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logger.error(f"Error updating decisions: {e}")
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return html.Div("No recent decisions")
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@self.app.callback(
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[Output('eth-cob-content', 'children'),
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Output('btc-cob-content', 'children')],
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[Input('interval-component', 'n_intervals')]
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)
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def update_cob_data(n):
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"""Update COB data"""
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try:
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eth_cob = self._render_cob_ladder("ETH/USDT")
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btc_cob = self._render_cob_ladder("BTC/USDT")
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return eth_cob, btc_cob
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except Exception as e:
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logger.error(f"Error updating COB: {e}")
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return html.Div("COB Error"), html.Div("COB Error")
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@self.app.callback(
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Output('training-metrics', 'children'),
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[Input('interval-component', 'n_intervals')]
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)
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def update_training_metrics(n):
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"""Update training metrics"""
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try:
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return self._render_training_metrics()
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except Exception as e:
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logger.error(f"Error updating training metrics: {e}")
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return html.Div("Training metrics unavailable")
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@self.app.callback(
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Output('closed-trades-table', 'children'),
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[Input('interval-component', 'n_intervals')]
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)
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def update_closed_trades(n):
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"""Update closed trades table"""
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try:
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# Return the table wrapped in a Div
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return html.Div(self._render_closed_trades())
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except Exception as e:
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logger.error(f"Error updating closed trades: {e}")
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return html.Div("No trades")
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# Trading control callbacks
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@self.app.callback(
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Output('manual-buy-btn', 'children'),
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[Input('manual-buy-btn', 'n_clicks')],
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prevent_initial_call=True
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)
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def handle_manual_buy(n_clicks):
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"""Handle manual buy button"""
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if n_clicks:
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self._execute_manual_trade("BUY")
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return "BUY ✓"
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return "BUY"
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@self.app.callback(
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Output('manual-sell-btn', 'children'),
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[Input('manual-sell-btn', 'n_clicks')],
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prevent_initial_call=True
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)
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def handle_manual_sell(n_clicks):
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"""Handle manual sell button"""
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if n_clicks:
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self._execute_manual_trade("SELL")
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return "SELL ✓"
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return "SELL"
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@self.app.callback(
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Output('leverage-display', 'children'),
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[Input('leverage-slider', 'value')]
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)
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def update_leverage_display(leverage_value):
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"""Update leverage display"""
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return f"{leverage_value}x"
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@self.app.callback(
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Output('clear-session-btn', 'children'),
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[Input('clear-session-btn', 'n_clicks')],
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prevent_initial_call=True
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)
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def handle_clear_session(n_clicks):
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"""Handle clear session button"""
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if n_clicks:
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self._clear_session()
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return "Cleared ✓"
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return "Clear Session"
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def _build_dashboard_data(self) -> DashboardModel:
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"""Build dashboard data model from current state"""
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builder = DashboardDataBuilder()
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# Basic info
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builder.set_basic_info(
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title="Live Scalping Dashboard (Templated)",
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subtitle="Template-based MVC Architecture",
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refresh_interval=1000
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)
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# Get current metrics
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current_price = self._get_current_price("ETH/USDT")
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portfolio_value = 10000.0 + self.session_pnl
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mexc_status = "Connected" if self.trading_executor else "Disconnected"
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# Add metrics
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builder.add_metric("current-price", "Current Price", current_price or 0, "currency")
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builder.add_metric("session-pnl", "Session PnL", self.session_pnl, "currency")
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builder.add_metric("current-position", "Position", self.current_position, "number")
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builder.add_metric("trade-count", "Trades", len(self.session_trades), "number")
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builder.add_metric("portfolio-value", "Portfolio", portfolio_value, "currency")
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builder.add_metric("mexc-status", "MEXC Status", mexc_status, "text")
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# Trading controls
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builder.set_trading_controls(leverage=10, leverage_range=(1, 50))
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# Recent decisions (sample data for now)
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builder.add_recent_decision(datetime.now(), "BUY", "ETH/USDT", 0.85, current_price or 3425.67)
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# COB data (sample)
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builder.add_cob_data("ETH/USDT", "eth-cob-content", 25000.0, 7.3, [])
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builder.add_cob_data("BTC/USDT", "btc-cob-content", 35000.0, 0.88, [])
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# Model statuses
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builder.add_model_status("DQN", True)
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builder.add_model_status("CNN", True)
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builder.add_model_status("Transformer", False)
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builder.add_model_status("COB-RL", True)
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# Training metrics
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builder.add_training_metric("DQN Loss", 0.0234)
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builder.add_training_metric("CNN Accuracy", 0.876)
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builder.add_training_metric("Training Steps", 15420)
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# Performance stats
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builder.add_performance_stat("Win Rate", 68.5)
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builder.add_performance_stat("Avg Trade", 8.34)
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builder.add_performance_stat("Sharpe Ratio", 1.82)
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return builder.build()
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def _get_current_price(self, symbol: str) -> Optional[float]:
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"""Get current price for symbol"""
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try:
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if self.data_provider:
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return self.data_provider.get_current_price(symbol)
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return 3425.67 # Sample price
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except Exception as e:
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logger.error(f"Error getting price for {symbol}: {e}")
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return None
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def _create_price_chart(self, symbol: str) -> go.Figure:
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"""Create price chart"""
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try:
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# Get price data
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df = self._get_chart_data(symbol)
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|
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if df is None or df.empty:
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return go.Figure().add_annotation(
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text="No data available",
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xref="paper", yref="paper",
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x=0.5, y=0.5, showarrow=False
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)
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|
|
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# Create candlestick chart
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fig = go.Figure(data=[go.Candlestick(
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x=df.index,
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open=df['open'],
|
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high=df['high'],
|
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low=df['low'],
|
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close=df['close'],
|
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name=symbol
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)])
|
|
|
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fig.update_layout(
|
|
title=f"{symbol} Price Chart",
|
|
xaxis_title="Time",
|
|
yaxis_title="Price (USDT)",
|
|
height=500,
|
|
showlegend=False
|
|
)
|
|
|
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return fig
|
|
|
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except Exception as e:
|
|
logger.error(f"Error creating chart for {symbol}: {e}")
|
|
return go.Figure()
|
|
|
|
def _get_chart_data(self, symbol: str) -> Optional[pd.DataFrame]:
|
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"""Get chart data for symbol"""
|
|
try:
|
|
if self.data_provider:
|
|
return self.data_provider.get_historical_data(symbol, "1m", 100)
|
|
|
|
# Sample data
|
|
import numpy as np
|
|
dates = pd.date_range(start='2024-01-01', periods=100, freq='1min')
|
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base_price = 3425.67
|
|
|
|
df = pd.DataFrame({
|
|
'open': base_price + np.random.randn(100) * 10,
|
|
'high': base_price + np.random.randn(100) * 15,
|
|
'low': base_price + np.random.randn(100) * 15,
|
|
'close': base_price + np.random.randn(100) * 10,
|
|
'volume': np.random.randint(100, 1000, 100)
|
|
}, index=dates)
|
|
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting chart data: {e}")
|
|
return None
|
|
|
|
def _get_recent_decisions(self) -> List[Dict]:
|
|
"""Get recent AI decisions"""
|
|
# Sample decisions for now
|
|
return [
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|
{
|
|
"timestamp": datetime.now().strftime("%H:%M:%S"),
|
|
"action": "BUY",
|
|
"symbol": "ETH/USDT",
|
|
"confidence": 85.3,
|
|
"price": 3425.67
|
|
},
|
|
{
|
|
"timestamp": datetime.now().strftime("%H:%M:%S"),
|
|
"action": "HOLD",
|
|
"symbol": "BTC/USDT",
|
|
"confidence": 62.1,
|
|
"price": 45123.45
|
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}
|
|
]
|
|
|
|
def _render_decisions(self, decisions: List[Dict]) -> List[html.Div]:
|
|
"""Render recent decisions"""
|
|
items = []
|
|
for decision in decisions:
|
|
border_class = {
|
|
'BUY': 'border-success bg-success bg-opacity-10',
|
|
'SELL': 'border-danger bg-danger bg-opacity-10'
|
|
}.get(decision['action'], 'border-secondary bg-secondary bg-opacity-10')
|
|
|
|
items.append(
|
|
html.Div([
|
|
html.Small(decision['timestamp'], className="text-muted"),
|
|
html.Br(),
|
|
html.Strong(f"{decision['action']} - {decision['symbol']}"),
|
|
html.Br(),
|
|
html.Small(f"Confidence: {decision['confidence']}% | Price: ${decision['price']}")
|
|
], className=f"mb-2 p-2 border-start border-3 {border_class}")
|
|
)
|
|
|
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return items
|
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|
|
def _render_cob_ladder(self, symbol: str) -> html.Div:
|
|
"""Render COB ladder for symbol"""
|
|
# Sample COB data
|
|
return html.Table([
|
|
html.Thead([
|
|
html.Tr([
|
|
html.Th("Size"),
|
|
html.Th("Price"),
|
|
html.Th("Total")
|
|
])
|
|
]),
|
|
html.Tbody([
|
|
html.Tr([
|
|
html.Td("1.5"),
|
|
html.Td("$3426.12"),
|
|
html.Td("$5139.18")
|
|
], className="ask-row"),
|
|
html.Tr([
|
|
html.Td("2.3"),
|
|
html.Td("$3425.89"),
|
|
html.Td("$7879.55")
|
|
], className="ask-row"),
|
|
html.Tr([
|
|
html.Td("1.8"),
|
|
html.Td("$3425.45"),
|
|
html.Td("$6165.81")
|
|
], className="bid-row"),
|
|
html.Tr([
|
|
html.Td("3.2"),
|
|
html.Td("$3425.12"),
|
|
html.Td("$10960.38")
|
|
], className="bid-row")
|
|
])
|
|
], className="table table-sm table-borderless")
|
|
|
|
def _render_training_metrics(self) -> html.Div:
|
|
"""Render training metrics"""
|
|
return html.Div([
|
|
# Model Status
|
|
html.Div([
|
|
html.H6("Model Status"),
|
|
html.Div([
|
|
html.Span("DQN: Training", className="model-status status-training"),
|
|
html.Span("CNN: Training", className="model-status status-training"),
|
|
html.Span("Transformer: Idle", className="model-status status-idle"),
|
|
html.Span("COB-RL: Training", className="model-status status-training")
|
|
])
|
|
], className="mb-3"),
|
|
|
|
# Training Metrics
|
|
html.Div([
|
|
html.H6("Training Metrics"),
|
|
html.Div([
|
|
html.Div([
|
|
html.Div([html.Small("DQN Loss:")], className="col-6"),
|
|
html.Div([html.Small("0.0234", className="fw-bold")], className="col-6")
|
|
], className="row mb-1"),
|
|
html.Div([
|
|
html.Div([html.Small("CNN Accuracy:")], className="col-6"),
|
|
html.Div([html.Small("87.6%", className="fw-bold")], className="col-6")
|
|
], className="row mb-1"),
|
|
html.Div([
|
|
html.Div([html.Small("Training Steps:")], className="col-6"),
|
|
html.Div([html.Small("15,420", className="fw-bold")], className="col-6")
|
|
], className="row mb-1")
|
|
])
|
|
], className="mb-3"),
|
|
|
|
# Performance Stats
|
|
html.Div([
|
|
html.H6("Performance"),
|
|
html.Div([
|
|
html.Div([
|
|
html.Div([html.Small("Win Rate:")], className="col-8"),
|
|
html.Div([html.Small("68.5%", className="fw-bold")], className="col-4")
|
|
], className="row mb-1"),
|
|
html.Div([
|
|
html.Div([html.Small("Avg Trade:")], className="col-8"),
|
|
html.Div([html.Small("$8.34", className="fw-bold")], className="col-4")
|
|
], className="row mb-1"),
|
|
html.Div([
|
|
html.Div([html.Small("Sharpe Ratio:")], className="col-8"),
|
|
html.Div([html.Small("1.82", className="fw-bold")], className="col-4")
|
|
], className="row mb-1")
|
|
])
|
|
])
|
|
])
|
|
|
|
def _render_closed_trades(self) -> html.Div:
|
|
"""Render closed trades table"""
|
|
if not self.closed_trades:
|
|
return html.Div("No closed trades yet.", className="alert alert-info mt-3")
|
|
|
|
# Create a DataFrame from closed trades
|
|
df_trades = pd.DataFrame(self.closed_trades)
|
|
|
|
# Format columns for display
|
|
df_trades['timestamp'] = pd.to_datetime(df_trades['timestamp']).dt.strftime('%Y-%m-%d %H:%M:%S')
|
|
df_trades['entry_price'] = df_trades['entry_price'].apply(lambda x: f"${x:,.2f}")
|
|
df_trades['exit_price'] = df_trades['exit_price'].apply(lambda x: f"${x:,.2f}")
|
|
df_trades['pnl'] = df_trades['pnl'].apply(lambda x: f"${x:,.2f}")
|
|
df_trades['profit_percentage'] = df_trades['profit_percentage'].apply(lambda x: f"{x:,.2f}%")
|
|
df_trades['size'] = df_trades['size'].apply(lambda x: f"{x:,.4f}")
|
|
df_trades['fees'] = df_trades['fees'].apply(lambda x: f"${x:,.2f}")
|
|
|
|
table_header = [html.Thead(html.Tr([html.Th(col) for col in df_trades.columns]))]
|
|
table_body = [html.Tbody([
|
|
html.Tr([html.Td(df_trades.iloc[i][col]) for col in df_trades.columns]) for i in range(len(df_trades))
|
|
])]
|
|
|
|
return html.Div(
|
|
html.Table(table_header + table_body, className="table table-striped table-hover table-sm"),
|
|
className="table-responsive"
|
|
)
|
|
|
|
def _execute_manual_trade(self, action: str):
|
|
"""Execute manual trade"""
|
|
try:
|
|
logger.info(f"MANUAL TRADE: {action} executed")
|
|
# Add to session trades
|
|
trade = {
|
|
"time": datetime.now(),
|
|
"action": action,
|
|
"symbol": "ETH/USDT",
|
|
"price": self._get_current_price("ETH/USDT") or 3425.67
|
|
}
|
|
self.session_trades.append(trade)
|
|
except Exception as e:
|
|
logger.error(f"Error executing manual trade: {e}")
|
|
|
|
def _clear_session(self):
|
|
"""Clear session data"""
|
|
self.session_trades = []
|
|
self.session_pnl = 0.0
|
|
self.current_position = 0.0
|
|
self.session_start_time = datetime.now()
|
|
logger.info("SESSION: Cleared")
|
|
|
|
def run_server(self, host='127.0.0.1', port=8052, debug=False):
|
|
"""Run the dashboard server"""
|
|
logger.info(f"TEMPLATED DASHBOARD: Starting at http://{host}:{port}")
|
|
self.app.run(host=host, port=port, debug=debug)
|
|
|
|
def _handle_unified_stream_data(self, data):
|
|
"""Placeholder for unified stream data handling."""
|
|
logger.debug(f"Received data from unified stream: {data}")
|
|
|
|
def _delayed_training_check(self):
|
|
"""Check and start training after a delay to allow initialization"""
|
|
try:
|
|
time.sleep(10) # Wait 10 seconds for initialization
|
|
logger.info("Checking if models need training activation...")
|
|
self._start_actual_training_if_needed()
|
|
except Exception as e:
|
|
logger.error(f"Error in delayed training check: {e}")
|
|
|
|
def _initialize_enhanced_training_system(self):
|
|
"""Initialize enhanced training system for model predictions"""
|
|
try:
|
|
# Try to import and initialize enhanced training system
|
|
from enhanced_realtime_training import EnhancedRealtimeTrainingSystem
|
|
|
|
self.training_system = EnhancedRealtimeTrainingSystem(
|
|
orchestrator=self.orchestrator,
|
|
data_provider=self.data_provider,
|
|
dashboard=self
|
|
)
|
|
|
|
# Initialize prediction storage
|
|
if not hasattr(self.orchestrator, 'recent_dqn_predictions'):
|
|
self.orchestrator.recent_dqn_predictions = {}
|
|
if not hasattr(self.orchestrator, 'recent_cnn_predictions'):
|
|
self.orchestrator.recent_cnn_predictions = {}
|
|
|
|
logger.info("TEMPLATED DASHBOARD: Enhanced training system initialized for model predictions")
|
|
|
|
except ImportError:
|
|
logger.warning("TEMPLATED DASHBOARD: Enhanced training system not available - using mock predictions")
|
|
self.training_system = None
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error initializing enhanced training system: {e}")
|
|
self.training_system = None
|
|
|
|
def _initialize_streaming(self):
|
|
"""Initialize data streaming"""
|
|
try:
|
|
self._start_websocket_streaming()
|
|
self._start_data_collection()
|
|
logger.info("TEMPLATED DASHBOARD: Data streaming initialized")
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error initializing streaming: {e}")
|
|
|
|
def _start_websocket_streaming(self):
|
|
"""Start WebSocket streaming for real-time data."""
|
|
ws_thread = threading.Thread(target=self._ws_worker, daemon=True)
|
|
ws_thread.start()
|
|
|
|
def _ws_worker(self):
|
|
try:
|
|
import websocket
|
|
import json # Added import
|
|
def on_message(ws, message):
|
|
try:
|
|
data = json.loads(message)
|
|
if 'k' in data:
|
|
kline = data['k']
|
|
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']),
|
|
'volume': float(kline['v']),
|
|
}
|
|
self.ws_price_cache['ETHUSDT'] = tick_record['price']
|
|
self.current_prices['ETH/USDT'] = tick_record['price']
|
|
self.tick_cache.append(tick_record)
|
|
if len(self.tick_cache) > 1000:
|
|
self.tick_cache.pop(0)
|
|
except Exception as e:
|
|
logger.warning(f"TEMPLATED DASHBOARD: WebSocket message error: {e}")
|
|
def on_error(ws, error):
|
|
logger.error(f"TEMPLATED DASHBOARD: WebSocket error: {error}")
|
|
self.is_streaming = False
|
|
def on_close(ws, close_status_code, close_msg):
|
|
logger.warning("TEMPLATED DASHBOARD: WebSocket connection closed")
|
|
self.is_streaming = False
|
|
def on_open(ws):
|
|
logger.info("TEMPLATED DASHBOARD: WebSocket connected")
|
|
self.is_streaming = True
|
|
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"TEMPLATED DASHBOARD: WebSocket worker error: {e}")
|
|
self.is_streaming = False
|
|
|
|
def _start_data_collection(self):
|
|
"""Start background data collection"""
|
|
data_thread = threading.Thread(target=self._data_worker, daemon=True)
|
|
data_thread.start()
|
|
|
|
def _data_worker(self):
|
|
while True:
|
|
try:
|
|
self._update_session_metrics()
|
|
time.sleep(5)
|
|
except Exception as e:
|
|
logger.warning(f"TEMPLATED DASHBOARD: Data collection error: {e}")
|
|
time.sleep(10)
|
|
|
|
def _update_session_metrics(self):
|
|
"""Update session P&L and total fees from closed trades."""
|
|
try:
|
|
closed_trades = []
|
|
if self.trading_executor and hasattr(self.trading_executor, 'get_closed_trades'):
|
|
closed_trades = self.trading_executor.get_closed_trades()
|
|
self.closed_trades = closed_trades
|
|
if closed_trades:
|
|
self.session_pnl = sum(trade.get('pnl', 0) for trade in closed_trades)
|
|
self.total_fees = sum(trade.get('fees', 0) for trade in closed_trades)
|
|
else:
|
|
self.session_pnl = 0.0
|
|
self.total_fees = 0.0
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error updating session metrics: {e}")
|
|
|
|
def _connect_to_orchestrator(self):
|
|
"""Connect to orchestrator for real trading signals"""
|
|
try:
|
|
if self.orchestrator and hasattr(self.orchestrator, 'add_decision_callback'):
|
|
import asyncio # Added import
|
|
# from dataclasses import asdict # Moved asdict to top-level import
|
|
|
|
def connect_worker():
|
|
try:
|
|
loop = asyncio.new_event_loop()
|
|
asyncio.set_event_loop(loop)
|
|
# No need to run_until_complete here, just register the callback
|
|
self.orchestrator.add_decision_callback(self._on_trading_decision)
|
|
logger.info("TEMPLATED DASHBOARD: Successfully connected to orchestrator for trading signals.")
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Orchestrator connection worker failed: {e}")
|
|
thread = threading.Thread(target=connect_worker, daemon=True)
|
|
thread.start()
|
|
else:
|
|
logger.warning("TEMPLATED DASHBOARD: Orchestrator not available or doesn\'t support callbacks")
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error initiating orchestrator connection: {e}")
|
|
|
|
async def _on_trading_decision(self, decision):
|
|
"""Handle trading decision from orchestrator."""
|
|
try:
|
|
action = getattr(decision, 'action', decision.get('action'))
|
|
if action == 'HOLD':
|
|
return
|
|
symbol = getattr(decision, 'symbol', decision.get('symbol', 'ETH/USDT'))
|
|
if 'ETH' not in symbol.upper():
|
|
return
|
|
dashboard_decision = asdict(decision) if not isinstance(decision, dict) else decision.copy()
|
|
dashboard_decision['timestamp'] = datetime.now()
|
|
dashboard_decision['executed'] = False
|
|
self.recent_decisions.append(dashboard_decision)
|
|
if len(self.recent_decisions) > 200:
|
|
self.recent_decisions.pop(0)
|
|
logger.info(f"TEMPLATED DASHBOARD: [ORCHESTRATOR SIGNAL] Received: {action} for {symbol}")
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error handling trading decision: {e}")
|
|
|
|
def _initialize_cob_integration(self):
|
|
"""Initialize simple COB integration that works without async event loops"""
|
|
try:
|
|
logger.info("TEMPLATED DASHBOARD: Initializing simple COB integration for model feeding")
|
|
|
|
# Initialize COB data storage
|
|
self.cob_bucketed_data = {
|
|
'ETH/USDT': {},
|
|
'BTC/USDT': {}
|
|
}
|
|
self.cob_last_update: Dict[str, Optional[float]] = {
|
|
'ETH/USDT': None,
|
|
'BTC/USDT': None
|
|
} # Corrected type hint
|
|
|
|
# Start simple COB data collection
|
|
self._start_simple_cob_collection()
|
|
|
|
logger.info("TEMPLATED DASHBOARD: Simple COB integration initialized successfully")
|
|
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error initializing COB integration: {e}")
|
|
self.cob_integration = None
|
|
|
|
def _start_simple_cob_collection(self):
|
|
"""Start simple COB data collection using REST APIs (no async required)"""
|
|
try:
|
|
# threading and time already imported
|
|
|
|
def cob_collector():
|
|
"""Collect COB data using simple REST API calls"""
|
|
while True:
|
|
try:
|
|
# Collect data for both symbols
|
|
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
|
self._collect_simple_cob_data(symbol)
|
|
|
|
# Sleep for 1 second between collections
|
|
time.sleep(1)
|
|
except Exception as e:
|
|
logger.debug(f"TEMPLATED DASHBOARD: Error in COB collection: {e}")
|
|
time.sleep(5) # Wait longer on error
|
|
|
|
# Start collector in background thread
|
|
cob_thread = threading.Thread(target=cob_collector, daemon=True)
|
|
cob_thread.start()
|
|
|
|
logger.info("TEMPLATED DASHBOARD: Simple COB data collection started")
|
|
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error starting COB collection: {e}")
|
|
|
|
def _collect_simple_cob_data(self, symbol: str):
|
|
"""Collect simple COB data using Binance REST API"""
|
|
try:
|
|
import requests # Added import
|
|
# time already imported
|
|
|
|
# Use Binance REST API for order book data
|
|
binance_symbol = symbol.replace('/', '')
|
|
url = f"https://api.binance.com/api/v3/depth?symbol={binance_symbol}&limit=500"
|
|
|
|
response = requests.get(url, timeout=5)
|
|
if response.status_code == 200:
|
|
data = response.json()
|
|
|
|
# Process order book data
|
|
bids = []
|
|
asks = []
|
|
|
|
# Process bids (buy orders)
|
|
for bid in data['bids'][:100]: # Top 100 levels
|
|
price = float(bid[0])
|
|
size = float(bid[1])
|
|
bids.append({
|
|
'price': price,
|
|
'size': size,
|
|
'total': price * size
|
|
})
|
|
|
|
# Process asks (sell orders)
|
|
for ask in data['asks'][:100]: # Top 100 levels
|
|
price = float(ask[0])
|
|
size = float(ask[1])
|
|
asks.append({
|
|
'price': price,
|
|
'size': size,
|
|
'total': price * size
|
|
})
|
|
|
|
# Calculate statistics
|
|
if bids and asks:
|
|
best_bid = max(bids, key=lambda x: x['price'])
|
|
best_ask = min(asks, key=lambda x: x['price'])
|
|
mid_price = (best_bid['price'] + best_ask['price']) / 2
|
|
spread_bps = ((best_ask['price'] - best_bid['price']) / mid_price) * 10000 if mid_price > 0 else 0
|
|
|
|
total_bid_liquidity = sum(bid['total'] for bid in bids[:20])
|
|
total_ask_liquidity = sum(ask['total'] for ask in asks[:20])
|
|
total_liquidity = total_bid_liquidity + total_ask_liquidity
|
|
imbalance = (total_bid_liquidity - total_ask_liquidity) / total_liquidity if total_liquidity > 0 else 0
|
|
|
|
# Create COB snapshot
|
|
cob_snapshot = {
|
|
'symbol': symbol,
|
|
'timestamp': time.time(),
|
|
'bids': bids,
|
|
'asks': asks,
|
|
'stats': {
|
|
'mid_price': mid_price,
|
|
'spread_bps': spread_bps,
|
|
'total_bid_liquidity': total_bid_liquidity,
|
|
'total_ask_liquidity': total_ask_liquidity,
|
|
'imbalance': imbalance,
|
|
'exchanges_active': ['Binance']
|
|
}
|
|
}
|
|
|
|
# Store in history (keep last 15 seconds)
|
|
self.cob_data_history[symbol].append(cob_snapshot)
|
|
if len(self.cob_data_history[symbol]) > 15: # Keep 15 seconds
|
|
# Use slicing to remove old elements from deque to ensure correct behavior
|
|
while len(self.cob_data_history[symbol]) > 15:
|
|
self.cob_data_history[symbol].popleft()
|
|
|
|
# Update latest data
|
|
self.latest_cob_data[symbol] = cob_snapshot
|
|
self.cob_last_update[symbol] = time.time()
|
|
|
|
# Generate bucketed data for models
|
|
self._generate_bucketed_cob_data(symbol, cob_snapshot)
|
|
|
|
logger.debug(f"TEMPLATED DASHBOARD: COB data collected for {symbol}: {len(bids)} bids, {len(asks)} asks")
|
|
|
|
except Exception as e:
|
|
logger.debug(f"TEMPLATED DASHBOARD: Error collecting COB data for {symbol}: {e}")
|
|
|
|
def _generate_bucketed_cob_data(self, symbol: str, cob_snapshot: dict):
|
|
"""Generate bucketed COB data for model feeding"""
|
|
try:
|
|
# Create price buckets (1 basis point granularity)
|
|
bucket_size_bps = 1.0
|
|
mid_price = cob_snapshot['stats']['mid_price']
|
|
|
|
# Initialize buckets
|
|
buckets = {}
|
|
|
|
# Process bids into buckets
|
|
for bid in cob_snapshot['bids']:
|
|
price_offset_bps = ((bid['price'] - mid_price) / mid_price) * 10000
|
|
bucket_key = int(price_offset_bps / bucket_size_bps)
|
|
|
|
if bucket_key not in buckets:
|
|
buckets[bucket_key] = {'bid_volume': 0, 'ask_volume': 0}
|
|
|
|
buckets[bucket_key]['bid_volume'] += bid['total']
|
|
|
|
# Process asks into buckets
|
|
for ask in cob_snapshot['asks']:
|
|
price_offset_bps = ((ask['price'] - mid_price) / mid_price) * 10000
|
|
bucket_key = int(price_offset_bps / bucket_size_bps)
|
|
|
|
if bucket_key not in buckets:
|
|
buckets[bucket_key] = {'bid_volume': 0, 'ask_volume': 0}
|
|
|
|
buckets[bucket_key]['ask_volume'] += ask['total']
|
|
|
|
# Store bucketed data
|
|
self.cob_bucketed_data[symbol] = {
|
|
'timestamp': cob_snapshot['timestamp'],
|
|
'mid_price': mid_price,
|
|
'buckets': buckets,
|
|
'bucket_size_bps': bucket_size_bps
|
|
}
|
|
|
|
# Feed to models
|
|
self._feed_cob_data_to_models(symbol, cob_snapshot)
|
|
|
|
except Exception as e:
|
|
logger.debug(f"TEMPLATED DASHBOARD: Error generating bucketed COB data: {e}")
|
|
|
|
def _calculate_cumulative_imbalance(self, symbol: str) -> Dict[str, float]:
|
|
"""Calculate average imbalance over multiple time windows."""
|
|
stats = {}
|
|
now = time.time()
|
|
history = self.cob_data_history.get(symbol)
|
|
|
|
if not history:
|
|
return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
|
|
|
|
periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
|
|
|
|
for name, duration in periods.items():
|
|
recent_imbalances = []
|
|
for snap in history:
|
|
# Check if snap is a valid dict with timestamp and stats
|
|
if isinstance(snap, dict) and 'timestamp' in snap and (now - snap['timestamp'] <= duration) and 'stats' in snap and snap['stats']:
|
|
imbalance = snap['stats'].get('imbalance')
|
|
if imbalance is not None:
|
|
recent_imbalances.append(imbalance)
|
|
|
|
if recent_imbalances:
|
|
stats[name] = sum(recent_imbalances) / len(recent_imbalances)
|
|
else:
|
|
stats[name] = 0.0
|
|
|
|
# Debug logging to verify cumulative imbalance calculation
|
|
if any(value != 0.0 for value in stats.values()):
|
|
logger.debug(f"TEMPLATED DASHBOARD: [CUMULATIVE-IMBALANCE] {symbol}: {stats}")
|
|
|
|
return stats
|
|
|
|
def _feed_cob_data_to_models(self, symbol: str, cob_snapshot: dict):
|
|
"""Feed COB data to models for training and inference"""
|
|
try:
|
|
# Calculate cumulative imbalance for model feeding
|
|
cumulative_imbalance = self._calculate_cumulative_imbalance(symbol) # Assumes _calculate_cumulative_imbalance is available
|
|
|
|
history_data = {
|
|
'symbol': symbol,
|
|
'current_snapshot': cob_snapshot,
|
|
'history': list(self.cob_data_history[symbol]), # Convert deque to list for consistent slicing
|
|
'bucketed_data': self.cob_bucketed_data[symbol],
|
|
'cumulative_imbalance': cumulative_imbalance, # Add cumulative imbalance
|
|
'timestamp': cob_snapshot['timestamp']
|
|
}
|
|
|
|
# Pass to orchestrator for model feeding
|
|
if self.orchestrator and hasattr(self.orchestrator, 'feed_cob_data'):
|
|
self.orchestrator.feed_cob_data(symbol, history_data) # Assumes feed_cob_data exists in orchestrator
|
|
|
|
except Exception as e:
|
|
logger.debug(f"TEMPLATED DASHBOARD: Error feeding COB data to models: {e}")
|
|
|
|
def _is_signal_generation_active(self) -> bool:
|
|
"""Check if signal generation is active (e.g., models are loaded and running)"""
|
|
# For now, return true to always generate signals
|
|
# In a real system, this would check model loading status, training status, etc.
|
|
return True # Simplified for initial integration
|
|
|
|
def _start_signal_generation_loop(self):
|
|
"""Start signal generation loop to ensure continuous trading signals"""
|
|
try:
|
|
def signal_worker():
|
|
logger.info("TEMPLATED DASHBOARD: Signal generation worker started")
|
|
while True:
|
|
try:
|
|
# Ensure signal generation is active before processing
|
|
if self._is_signal_generation_active():
|
|
symbol = 'ETH/USDT' # Focus on ETH for now
|
|
current_price = self._get_current_price(symbol)
|
|
if current_price:
|
|
# Generate a momentum signal (simplified for demo)
|
|
signal = self._generate_momentum_signal(symbol, current_price) # Assumes _generate_momentum_signal is available
|
|
if signal:
|
|
self._process_dashboard_signal(signal) # Assumes _process_dashboard_signal is available
|
|
|
|
# Generate a DQN signal if enabled
|
|
if self.dqn_inference_enabled:
|
|
dqn_signal = self._generate_dqn_signal(symbol, current_price) # Assumes _generate_dqn_signal is available
|
|
if dqn_signal:
|
|
self._process_dashboard_signal(dqn_signal)
|
|
|
|
# Generate a CNN pivot signal if enabled
|
|
if self.cnn_inference_enabled:
|
|
cnn_signal = self._get_cnn_pivot_prediction() # Assumes _get_cnn_pivot_prediction is available
|
|
if cnn_signal:
|
|
self._process_dashboard_signal(cnn_signal)
|
|
|
|
# Update session metrics every 1 second interval to reflect new trades
|
|
self._update_session_metrics()
|
|
|
|
time.sleep(1) # Run every second for signal generation
|
|
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error in signal worker: {e}")
|
|
time.sleep(5) # Longer sleep on error
|
|
|
|
signal_thread = threading.Thread(target=signal_worker, daemon=True)
|
|
signal_thread.start()
|
|
logger.info("TEMPLATED DASHBOARD: Signal generation loop started")
|
|
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error starting signal generation loop: {e}")
|
|
|
|
def _start_actual_training_if_needed(self):
|
|
"""Start actual model training with real data collection and training loops"""
|
|
try:
|
|
if not self.orchestrator:
|
|
logger.warning("TEMPLATED DASHBOARD: No orchestrator available for training")
|
|
return
|
|
logger.info("TEMPLATED DASHBOARD: TRAINING: Starting actual training system with real data collection")
|
|
self._start_real_training_system()
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error starting comprehensive training system: {e}")
|
|
|
|
def _start_real_training_system(self):
|
|
"""Start real training system with data collection and actual model training"""
|
|
try:
|
|
# Training performance metrics
|
|
self.training_performance = {
|
|
'decision': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
|
'cob_rl': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
|
'dqn': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
|
'cnn': {'inference_times': [], 'training_times': [], 'total_calls': 0},
|
|
'transformer': {'inference_times': [], 'training_times': [], 'total_calls': 0} # Added for transformer
|
|
}
|
|
|
|
def training_coordinator():
|
|
logger.info("TEMPLATED DASHBOARD: TRAINING: High-frequency training coordinator started")
|
|
training_iteration = 0
|
|
last_dqn_training = 0
|
|
last_cnn_training = 0
|
|
last_decision_training = 0
|
|
last_cob_rl_training = 0
|
|
last_transformer_training = 0 # For transformer
|
|
|
|
while True:
|
|
try:
|
|
training_iteration += 1
|
|
current_time = time.time()
|
|
market_data = self._collect_training_data() # Assumes _collect_training_data is available
|
|
|
|
if market_data:
|
|
logger.debug(f"TEMPLATED DASHBOARD: TRAINING: Collected {len(market_data)} market data points for training")
|
|
|
|
# High-frequency training for split-second decisions
|
|
# Train decision fusion and COB RL as fast as hardware allows
|
|
if current_time - last_decision_training > 0.1: # Every 100ms
|
|
start_time = time.time()
|
|
self._perform_real_decision_training(market_data) # Assumes _perform_real_decision_training is available
|
|
training_time = time.time() - start_time
|
|
self.training_performance['decision']['training_times'].append(training_time)
|
|
self.training_performance['decision']['total_calls'] += 1
|
|
last_decision_training = current_time
|
|
|
|
# Keep only last 100 measurements
|
|
if len(self.training_performance['decision']['training_times']) > 100:
|
|
self.training_performance['decision']['training_times'] = self.training_performance['decision']['training_times'][-100:]
|
|
|
|
# Advanced Transformer Training (every 200ms for comprehensive features)
|
|
if current_time - last_transformer_training > 0.2: # Every 200ms for transformer
|
|
start_time = time.time()
|
|
self._perform_real_transformer_training(market_data) # Assumes _perform_real_transformer_training is available
|
|
training_time = time.time() - start_time
|
|
self.training_performance['transformer']['training_times'].append(training_time)
|
|
self.training_performance['transformer']['total_calls'] += 1
|
|
last_transformer_training = current_time # Update last training time
|
|
|
|
# Keep only last 100 measurements
|
|
if len(self.training_performance['transformer']['training_times']) > 100:
|
|
self.training_performance['transformer']['training_times'] = self.training_performance['transformer']['training_times'][-100:]
|
|
|
|
if current_time - last_cob_rl_training > 0.1: # Every 100ms
|
|
start_time = time.time()
|
|
self._perform_real_cob_rl_training(market_data) # Assumes _perform_real_cob_rl_training is available
|
|
training_time = time.time() - start_time
|
|
self.training_performance['cob_rl']['training_times'].append(training_time)
|
|
self.training_performance['cob_rl']['total_calls'] += 1
|
|
last_cob_rl_training = current_time
|
|
|
|
# Keep only last 100 measurements
|
|
if len(self.training_performance['cob_rl']['training_times']) > 100:
|
|
self.training_performance['cob_rl']['training_times'] = self.training_performance['cob_rl']['training_times'][-100:]
|
|
|
|
# Standard frequency for larger models
|
|
if current_time - last_dqn_training > 30:
|
|
start_time = time.time()
|
|
self._perform_real_dqn_training(market_data) # Assumes _perform_real_dqn_training is available
|
|
training_time = time.time() - start_time
|
|
self.training_performance['dqn']['training_times'].append(training_time)
|
|
self.training_performance['dqn']['total_calls'] += 1
|
|
last_dqn_training = current_time
|
|
|
|
if len(self.training_performance['dqn']['training_times']) > 50:
|
|
self.training_performance['dqn']['training_times'] = self.training_performance['dqn']['training_times'][-50:]
|
|
|
|
if current_time - last_cnn_training > 45:
|
|
start_time = time.time()
|
|
self._perform_real_cnn_training(market_data) # Assumes _perform_real_cnn_training is available
|
|
training_time = time.time() - start_time
|
|
self.training_performance['cnn']['training_times'].append(training_time)
|
|
self.training_performance['cnn']['total_calls'] += 1
|
|
last_cnn_training = current_time
|
|
|
|
if len(self.training_performance['cnn']['training_times']) > 50:
|
|
self.training_performance['cnn']['training_times'] = self.training_performance['cnn']['training_times'][-50:]
|
|
|
|
self._update_training_progress(training_iteration) # Assumes _update_training_progress is available
|
|
|
|
# Log performance metrics every 100 iterations
|
|
if training_iteration % 100 == 0:
|
|
self._log_training_performance() # Assumes _log_training_performance is available
|
|
logger.info(f"TEMPLATED DASHBOARD: TRAINING: Iteration {training_iteration} - High-frequency training active")
|
|
|
|
# Minimal sleep for maximum responsiveness
|
|
time.sleep(0.05) # 50ms sleep for 20Hz training loop
|
|
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: TRAINING: Error in training iteration {training_iteration}: {e}")
|
|
time.sleep(1) # Shorter error recovery
|
|
|
|
training_thread = threading.Thread(target=training_coordinator, daemon=True)
|
|
training_thread.start()
|
|
logger.info("TEMPLATED DASHBOARD: Real training system started")
|
|
|
|
except Exception as e:
|
|
logger.error(f"TEMPLATED DASHBOARD: Error starting real training system: {e}")
|
|
|
|
def create_templated_dashboard(data_provider: Optional[DataProvider] = None,
|
|
orchestrator: Optional[TradingOrchestrator] = None,
|
|
trading_executor: Optional[TradingExecutor] = None) -> TemplatedTradingDashboard:
|
|
"""Create templated trading dashboard"""
|
|
return TemplatedTradingDashboard(data_provider, orchestrator, trading_executor) |