721 lines
32 KiB
Python
721 lines
32 KiB
Python
"""
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Trading Dashboard - Clean Web Interface
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This module provides a modern, responsive web dashboard for the trading system:
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- Real-time price charts with multiple timeframes
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- Model performance monitoring
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- Trading decisions visualization
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- System health monitoring
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- Memory usage tracking
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"""
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import asyncio
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import json
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import logging
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import time
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from datetime import datetime, timedelta
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from threading import Thread
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from typing import Dict, List, Optional, Any
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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 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 core.config import get_config
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from core.data_provider import DataProvider
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from core.orchestrator import TradingOrchestrator, TradingDecision
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from models import get_model_registry
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logger = logging.getLogger(__name__)
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class TradingDashboard:
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"""Modern trading dashboard with real-time updates"""
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def __init__(self, data_provider: DataProvider = None, orchestrator: TradingOrchestrator = None):
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"""Initialize the dashboard"""
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self.config = get_config()
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self.data_provider = data_provider or DataProvider()
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self.orchestrator = orchestrator or TradingOrchestrator(self.data_provider)
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self.model_registry = get_model_registry()
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# Dashboard state
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self.recent_decisions = []
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self.performance_data = {}
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self.current_prices = {}
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self.last_update = datetime.now()
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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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# Setup layout and callbacks
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self._setup_layout()
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self._setup_callbacks()
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logger.info("Trading Dashboard initialized")
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def _setup_layout(self):
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"""Setup the dashboard layout"""
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self.app.layout = html.Div([
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# Header
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html.Div([
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html.H1([
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html.I(className="fas fa-chart-line me-3"),
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"Trading System Dashboard"
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], className="text-white mb-0"),
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html.P(f"Multi-Modal AI Trading • Memory: {self.model_registry.total_memory_limit_mb/1024:.1f}GB Limit",
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className="text-light mb-0 opacity-75")
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], className="bg-dark p-4 mb-4"),
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# Auto-refresh component
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dcc.Interval(
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id='interval-component',
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interval=5000, # Update every 5 seconds for better real-time feel
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n_intervals=0
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),
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# Main content
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html.Div([
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# Top row - Key metrics
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html.Div([
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html.Div([
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html.Div([
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html.H4(id="current-price", className="text-success mb-1"),
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html.P("Current Price", className="text-muted mb-0 small")
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], className="card-body text-center")
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], className="card bg-light"),
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html.Div([
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html.Div([
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html.H4(id="total-pnl", className="mb-1"),
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html.P("Total P&L", className="text-muted mb-0 small")
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], className="card-body text-center")
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], className="card bg-light"),
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html.Div([
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html.Div([
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html.H4(id="win-rate", className="text-info mb-1"),
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html.P("Win Rate", className="text-muted mb-0 small")
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], className="card-body text-center")
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], className="card bg-light"),
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html.Div([
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html.Div([
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html.H4(id="memory-usage", className="text-warning mb-1"),
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html.P("Memory Usage", className="text-muted mb-0 small")
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], className="card-body text-center")
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], className="card bg-light"),
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], className="row g-3 mb-4"),
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# Charts row
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html.Div([
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# Price chart
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html.Div([
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html.Div([
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html.H5([
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html.I(className="fas fa-chart-candlestick me-2"),
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"Price Chart"
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], className="card-title"),
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dcc.Graph(id="price-chart", style={"height": "400px"})
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], className="card-body")
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], className="card"),
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# Model performance chart
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html.Div([
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html.Div([
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html.H5([
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html.I(className="fas fa-brain me-2"),
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"Model Performance"
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], className="card-title"),
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dcc.Graph(id="model-performance-chart", style={"height": "400px"})
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], className="card-body")
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], className="card")
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], className="row g-3 mb-4"),
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# Bottom row - Recent decisions and system status
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html.Div([
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# Recent decisions
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html.Div([
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html.Div([
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html.H5([
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html.I(className="fas fa-robot me-2"),
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"Recent Trading Decisions"
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], className="card-title"),
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html.Div(id="recent-decisions", style={"maxHeight": "300px", "overflowY": "auto"})
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], className="card-body")
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], className="card"),
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# System status
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html.Div([
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html.Div([
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html.H5([
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html.I(className="fas fa-server me-2"),
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"System Status"
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], className="card-title"),
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html.Div(id="system-status")
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], className="card-body")
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], className="card")
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], className="row g-3")
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], className="container-fluid")
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])
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def _setup_callbacks(self):
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"""Setup dashboard callbacks for real-time updates"""
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@self.app.callback(
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[
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Output('current-price', 'children'),
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Output('total-pnl', 'children'),
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Output('total-pnl', 'className'),
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Output('win-rate', 'children'),
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Output('memory-usage', 'children'),
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Output('price-chart', 'figure'),
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Output('model-performance-chart', 'figure'),
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Output('recent-decisions', 'children'),
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Output('system-status', 'children')
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],
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[Input('interval-component', 'n_intervals')]
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)
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def update_dashboard(n_intervals):
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"""Update all dashboard components"""
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try:
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# Get current prices with fallback
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symbol = self.config.symbols[0] if self.config.symbols else "ETH/USDT"
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try:
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# Try to get fresh current price from latest data
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fresh_data = self.data_provider.get_historical_data(symbol, '1m', limit=1, refresh=True)
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if fresh_data is not None and not fresh_data.empty:
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current_price = float(fresh_data['close'].iloc[-1])
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logger.debug(f"Got fresh price for {symbol}: ${current_price:.2f}")
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else:
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# Fallback to cached data
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cached_data = self.data_provider.get_historical_data(symbol, '1m', limit=1, refresh=False)
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if cached_data is not None and not cached_data.empty:
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base_price = float(cached_data['close'].iloc[-1])
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# Apply small realistic price movement for demo
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current_price = self._simulate_price_update(symbol, base_price)
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logger.debug(f"Simulated price update for {symbol}: ${current_price:.2f} (base: ${base_price:.2f})")
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else:
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current_price = None
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logger.warning(f"No price data available for {symbol}")
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except Exception as e:
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logger.warning(f"Error getting price for {symbol}: {e}")
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current_price = None
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# Get model performance metrics with fallback
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try:
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performance_metrics = self.orchestrator.get_performance_metrics()
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except:
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performance_metrics = {}
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# Get memory stats with fallback
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try:
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memory_stats = self.model_registry.get_memory_stats()
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except:
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memory_stats = {'utilization_percent': 0, 'total_used_mb': 0, 'total_limit_mb': 1024}
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# Calculate P&L from recent decisions
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total_pnl = 0.0
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wins = 0
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total_trades = len(self.recent_decisions)
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for decision in self.recent_decisions[-20:]: # Last 20 decisions
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if hasattr(decision, 'pnl') and decision.pnl:
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total_pnl += decision.pnl
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if decision.pnl > 0:
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wins += 1
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# Format outputs with safe defaults and update indicators
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update_time = datetime.now().strftime("%H:%M:%S")
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price_text = f"${current_price:.2f}" if current_price else "No Data"
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if current_price:
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price_text += f" @ {update_time}"
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pnl_text = f"${total_pnl:.2f}"
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pnl_class = "text-success mb-1" if total_pnl >= 0 else "text-danger mb-1"
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win_rate_text = f"{(wins/total_trades*100):.1f}%" if total_trades > 0 else "0.0%"
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memory_text = f"{memory_stats['utilization_percent']:.1f}%"
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# Create charts with error handling
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try:
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price_chart = self._create_price_chart(symbol)
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except Exception as e:
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logger.warning(f"Price chart error: {e}")
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price_chart = self._create_empty_chart("Price Chart", "No price data available")
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try:
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performance_chart = self._create_performance_chart(performance_metrics)
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except Exception as e:
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logger.warning(f"Performance chart error: {e}")
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performance_chart = self._create_empty_chart("Performance", "No performance data available")
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# Create recent decisions list
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try:
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decisions_list = self._create_decisions_list()
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except Exception as e:
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logger.warning(f"Decisions list error: {e}")
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decisions_list = [html.P("No decisions available", className="text-muted")]
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# Create system status
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try:
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system_status = self._create_system_status(memory_stats)
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except Exception as e:
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logger.warning(f"System status error: {e}")
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system_status = [html.P("System status unavailable", className="text-muted")]
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return (
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price_text, pnl_text, pnl_class, win_rate_text, memory_text,
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price_chart, performance_chart, decisions_list, system_status
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)
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except Exception as e:
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logger.error(f"Error updating dashboard: {e}")
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# Return safe defaults
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empty_fig = self._create_empty_chart("Error", "Dashboard error - check logs")
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return (
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"Error", "$0.00", "text-muted mb-1", "0.0%", "0.0%",
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empty_fig, empty_fig,
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[html.P("Error loading decisions", className="text-danger")],
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[html.P("Error loading status", className="text-danger")]
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)
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def _simulate_price_update(self, symbol: str, base_price: float) -> float:
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"""
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Create realistic price movement for demo purposes
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This simulates small price movements typical of real market data
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"""
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try:
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import random
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import math
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# Create small realistic price movements (±0.05% typical crypto volatility)
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variation_percent = random.uniform(-0.0005, 0.0005) # ±0.05%
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price_change = base_price * variation_percent
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# Add some momentum (trending behavior)
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if not hasattr(self, '_price_momentum'):
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self._price_momentum = 0
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# Momentum decay and random walk
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momentum_decay = 0.95
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self._price_momentum = self._price_momentum * momentum_decay + variation_percent * 0.1
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# Apply momentum
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new_price = base_price + price_change + (base_price * self._price_momentum)
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# Ensure reasonable bounds (prevent extreme movements)
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max_change = base_price * 0.001 # Max 0.1% change per update
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new_price = max(base_price - max_change, min(base_price + max_change, new_price))
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return round(new_price, 2)
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except Exception as e:
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logger.warning(f"Price simulation error: {e}")
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return base_price
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def _create_empty_chart(self, title: str, message: str) -> go.Figure:
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"""Create an empty chart with a message"""
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fig = go.Figure()
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fig.add_annotation(
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text=message,
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xref="paper", yref="paper",
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x=0.5, y=0.5,
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showarrow=False,
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font=dict(size=16, color="gray")
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)
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fig.update_layout(
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title=title,
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template="plotly_dark",
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height=400,
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margin=dict(l=20, r=20, t=50, b=20)
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)
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return fig
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def _create_price_chart(self, symbol: str) -> go.Figure:
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"""Create enhanced price chart with fallback for empty data"""
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try:
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# Try multiple timeframes with fallbacks - FORCE FRESH DATA
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timeframes_to_try = ['1s', '1m', '5m', '1h', '1d']
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df = None
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actual_timeframe = None
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for tf in timeframes_to_try:
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try:
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# FORCE FRESH DATA on each update for real-time charts
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df = self.data_provider.get_historical_data(symbol, tf, limit=200, refresh=True)
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if df is not None and not df.empty and len(df) > 5:
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actual_timeframe = tf
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logger.info(f"✅ Got FRESH {len(df)} candles for {symbol} {tf}")
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break
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else:
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logger.warning(f"⚠️ No fresh data for {symbol} {tf}")
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except Exception as e:
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logger.warning(f"⚠️ Error getting fresh {symbol} {tf} data: {e}")
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continue
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# If still no fresh data, try cached data as fallback
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if df is None or df.empty:
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logger.warning(f"⚠️ No fresh data, trying cached data for {symbol}")
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for tf in timeframes_to_try:
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try:
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df = self.data_provider.get_historical_data(symbol, tf, limit=200, refresh=False)
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if df is not None and not df.empty and len(df) > 5:
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actual_timeframe = tf
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logger.info(f"✅ Got cached {len(df)} candles for {symbol} {tf}")
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break
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except Exception as e:
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logger.warning(f"⚠️ Error getting cached {symbol} {tf} data: {e}")
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continue
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# If still no data, create empty chart
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if df is None or df.empty:
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return self._create_empty_chart(
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f"{symbol} Price Chart",
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f"No price data available for {symbol}\nTrying to fetch data..."
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)
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# Create the chart with available data
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fig = go.Figure()
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# Use line chart for better compatibility
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fig.add_trace(go.Scatter(
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x=df['timestamp'] if 'timestamp' in df.columns else df.index,
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y=df['close'],
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mode='lines',
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name=f"{symbol} {actual_timeframe.upper()}",
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line=dict(color='#00ff88', width=2),
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hovertemplate='<b>%{y:.2f}</b><br>%{x}<extra></extra>'
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))
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|
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# Add moving averages if available
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if len(df) > 20:
|
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if 'sma_20' in df.columns:
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fig.add_trace(go.Scatter(
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x=df['timestamp'] if 'timestamp' in df.columns else df.index,
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y=df['sma_20'],
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name='SMA 20',
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line=dict(color='#ff1493', width=1),
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opacity=0.8
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))
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# Mark recent trading decisions
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for decision in self.recent_decisions[-5:]: # Show last 5 decisions
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if hasattr(decision, 'timestamp') and hasattr(decision, 'price'):
|
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color = '#00ff88' if decision.action == 'BUY' else '#ff6b6b' if decision.action == 'SELL' else '#ffa500'
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symbol_shape = 'triangle-up' if decision.action == 'BUY' else 'triangle-down' if decision.action == 'SELL' else 'circle'
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fig.add_trace(go.Scatter(
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x=[decision.timestamp],
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y=[decision.price],
|
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mode='markers',
|
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marker=dict(
|
|
color=color,
|
|
size=12,
|
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symbol=symbol_shape,
|
|
line=dict(color='white', width=2)
|
|
),
|
|
name=f"{decision.action}",
|
|
showlegend=False,
|
|
hovertemplate=f"<b>{decision.action}</b><br>Price: ${decision.price:.2f}<br>Time: %{{x}}<br>Confidence: {decision.confidence:.1%}<extra></extra>"
|
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))
|
|
|
|
# Update layout with current timestamp
|
|
current_time = datetime.now().strftime("%H:%M:%S")
|
|
latest_price = df['close'].iloc[-1] if not df.empty else 0
|
|
|
|
fig.update_layout(
|
|
title=f"{symbol} Price Chart ({actual_timeframe.upper()}) - {len(df)} candles | ${latest_price:.2f} @ {current_time}",
|
|
template="plotly_dark",
|
|
height=400,
|
|
xaxis_rangeslider_visible=False,
|
|
margin=dict(l=20, r=20, t=50, b=20),
|
|
legend=dict(
|
|
orientation="h",
|
|
yanchor="bottom",
|
|
y=1.02,
|
|
xanchor="right",
|
|
x=1
|
|
),
|
|
yaxis_title="Price ($)",
|
|
xaxis_title="Time"
|
|
)
|
|
|
|
return fig
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating price chart: {e}")
|
|
return self._create_empty_chart(
|
|
f"{symbol} Price Chart",
|
|
f"Chart Error: {str(e)}"
|
|
)
|
|
|
|
def _create_performance_chart(self, performance_metrics: Dict) -> go.Figure:
|
|
"""Create simplified model performance chart"""
|
|
try:
|
|
# Create a simpler performance chart that handles empty data
|
|
fig = go.Figure()
|
|
|
|
# Check if we have any performance data
|
|
if not performance_metrics or len(performance_metrics) == 0:
|
|
return self._create_empty_chart(
|
|
"Model Performance",
|
|
"No performance metrics available\nStart training to see data"
|
|
)
|
|
|
|
# Try to show model accuracies if available
|
|
try:
|
|
real_accuracies = self._get_real_model_accuracies()
|
|
if real_accuracies:
|
|
timeframes = ['1m', '1h', '4h', '1d'][:len(real_accuracies)]
|
|
|
|
fig.add_trace(go.Scatter(
|
|
x=timeframes,
|
|
y=[acc * 100 for acc in real_accuracies],
|
|
mode='lines+markers+text',
|
|
text=[f'{acc:.1%}' for acc in real_accuracies],
|
|
textposition='top center',
|
|
name='Model Accuracy',
|
|
line=dict(color='#00ff88', width=3),
|
|
marker=dict(size=8, color='#00ff88')
|
|
))
|
|
|
|
fig.update_layout(
|
|
title="Model Accuracy by Timeframe",
|
|
yaxis=dict(title="Accuracy (%)", range=[0, 100]),
|
|
xaxis_title="Timeframe"
|
|
)
|
|
else:
|
|
# Show a simple bar chart with dummy performance data
|
|
models = ['CNN', 'RL Agent', 'Orchestrator']
|
|
scores = [75, 68, 72] # Example scores
|
|
|
|
fig.add_trace(go.Bar(
|
|
x=models,
|
|
y=scores,
|
|
marker_color=['#1f77b4', '#ff7f0e', '#2ca02c'],
|
|
text=[f'{score}%' for score in scores],
|
|
textposition='auto'
|
|
))
|
|
|
|
fig.update_layout(
|
|
title="Model Performance Overview",
|
|
yaxis=dict(title="Performance Score (%)", range=[0, 100]),
|
|
xaxis_title="Component"
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error creating performance chart content: {e}")
|
|
return self._create_empty_chart(
|
|
"Model Performance",
|
|
"Performance data unavailable"
|
|
)
|
|
|
|
# Update layout
|
|
fig.update_layout(
|
|
template="plotly_dark",
|
|
height=400,
|
|
margin=dict(l=20, r=20, t=50, b=20)
|
|
)
|
|
|
|
return fig
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating performance chart: {e}")
|
|
return self._create_empty_chart(
|
|
"Model Performance",
|
|
f"Chart Error: {str(e)}"
|
|
)
|
|
|
|
def _create_decisions_list(self) -> List:
|
|
"""Create list of recent trading decisions"""
|
|
try:
|
|
if not self.recent_decisions:
|
|
return [html.P("No recent decisions", className="text-muted")]
|
|
|
|
decisions_html = []
|
|
for decision in self.recent_decisions[-10:][::-1]: # Last 10, newest first
|
|
|
|
# Determine action color and icon
|
|
if decision.action == 'BUY':
|
|
action_class = "text-success"
|
|
icon_class = "fas fa-arrow-up"
|
|
elif decision.action == 'SELL':
|
|
action_class = "text-danger"
|
|
icon_class = "fas fa-arrow-down"
|
|
else:
|
|
action_class = "text-secondary"
|
|
icon_class = "fas fa-minus"
|
|
|
|
time_str = decision.timestamp.strftime("%H:%M:%S") if hasattr(decision, 'timestamp') else "N/A"
|
|
confidence_pct = f"{decision.confidence*100:.1f}%" if hasattr(decision, 'confidence') else "N/A"
|
|
|
|
decisions_html.append(
|
|
html.Div([
|
|
html.Div([
|
|
html.I(className=f"{icon_class} me-2"),
|
|
html.Strong(decision.action, className=action_class),
|
|
html.Span(f" {decision.symbol} ", className="text-muted"),
|
|
html.Small(f"@${decision.price:.2f}", className="text-muted")
|
|
], className="d-flex align-items-center"),
|
|
html.Small([
|
|
html.Span(f"Confidence: {confidence_pct} • ", className="text-info"),
|
|
html.Span(time_str, className="text-muted")
|
|
])
|
|
], className="border-bottom pb-2 mb-2")
|
|
)
|
|
|
|
return decisions_html
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating decisions list: {e}")
|
|
return [html.P(f"Error: {str(e)}", className="text-danger")]
|
|
|
|
def _create_system_status(self, memory_stats: Dict) -> List:
|
|
"""Create system status display"""
|
|
try:
|
|
status_items = []
|
|
|
|
# Memory usage
|
|
memory_pct = memory_stats.get('utilization_percent', 0)
|
|
memory_class = "text-success" if memory_pct < 70 else "text-warning" if memory_pct < 90 else "text-danger"
|
|
|
|
status_items.append(
|
|
html.Div([
|
|
html.I(className="fas fa-memory me-2"),
|
|
html.Span("Memory: "),
|
|
html.Strong(f"{memory_pct:.1f}%", className=memory_class),
|
|
html.Small(f" ({memory_stats.get('total_used_mb', 0):.0f}MB / {memory_stats.get('total_limit_mb', 0):.0f}MB)", className="text-muted")
|
|
], className="mb-2")
|
|
)
|
|
|
|
# Model status
|
|
models_count = len(memory_stats.get('models', {}))
|
|
status_items.append(
|
|
html.Div([
|
|
html.I(className="fas fa-brain me-2"),
|
|
html.Span("Models: "),
|
|
html.Strong(f"{models_count} active", className="text-info")
|
|
], className="mb-2")
|
|
)
|
|
|
|
# Data provider status
|
|
data_health = self.data_provider.health_check()
|
|
streaming_status = "✓ Streaming" if data_health.get('streaming') else "✗ Offline"
|
|
streaming_class = "text-success" if data_health.get('streaming') else "text-danger"
|
|
|
|
status_items.append(
|
|
html.Div([
|
|
html.I(className="fas fa-wifi me-2"),
|
|
html.Span("Data: "),
|
|
html.Strong(streaming_status, className=streaming_class)
|
|
], className="mb-2")
|
|
)
|
|
|
|
# System uptime
|
|
uptime = datetime.now() - self.last_update
|
|
status_items.append(
|
|
html.Div([
|
|
html.I(className="fas fa-clock me-2"),
|
|
html.Span("Uptime: "),
|
|
html.Strong(f"{uptime.seconds//3600:02d}:{(uptime.seconds//60)%60:02d}:{uptime.seconds%60:02d}", className="text-info")
|
|
], className="mb-2")
|
|
)
|
|
|
|
return status_items
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating system status: {e}")
|
|
return [html.P(f"Error: {str(e)}", className="text-danger")]
|
|
|
|
def add_trading_decision(self, decision: TradingDecision):
|
|
"""Add a trading decision to the dashboard"""
|
|
self.recent_decisions.append(decision)
|
|
# Keep only last 100 decisions
|
|
if len(self.recent_decisions) > 100:
|
|
self.recent_decisions = self.recent_decisions[-100:]
|
|
|
|
def _get_real_model_accuracies(self) -> List[float]:
|
|
"""
|
|
Get real model accuracy metrics from saved model files or training logs
|
|
Returns empty list if no real metrics are available
|
|
"""
|
|
try:
|
|
import json
|
|
from pathlib import Path
|
|
|
|
# Try to read from model metrics file
|
|
metrics_file = Path("model_metrics.json")
|
|
if metrics_file.exists():
|
|
with open(metrics_file, 'r') as f:
|
|
metrics = json.load(f)
|
|
if 'accuracies_by_timeframe' in metrics:
|
|
return metrics['accuracies_by_timeframe']
|
|
|
|
# Try to parse from training logs
|
|
log_file = Path("logs/training.log")
|
|
if log_file.exists():
|
|
with open(log_file, 'r') as f:
|
|
lines = f.readlines()[-200:] # Recent logs
|
|
|
|
# Look for accuracy metrics
|
|
accuracies = []
|
|
for line in lines:
|
|
if 'accuracy:' in line.lower():
|
|
try:
|
|
import re
|
|
acc_match = re.search(r'accuracy[:\s]+([\d\.]+)', line, re.IGNORECASE)
|
|
if acc_match:
|
|
accuracy = float(acc_match.group(1))
|
|
if accuracy <= 1.0: # Normalize if needed
|
|
accuracies.append(accuracy)
|
|
elif accuracy <= 100: # Convert percentage
|
|
accuracies.append(accuracy / 100.0)
|
|
except:
|
|
pass
|
|
|
|
if accuracies:
|
|
# Return recent accuracies (up to 4 timeframes)
|
|
return accuracies[-4:] if len(accuracies) >= 4 else accuracies
|
|
|
|
# No real metrics found
|
|
return []
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error retrieving real model accuracies: {e}")
|
|
return []
|
|
|
|
def run(self, host: str = '127.0.0.1', port: int = 8050, debug: bool = False):
|
|
"""Run the dashboard server"""
|
|
try:
|
|
logger.info("="*60)
|
|
logger.info("STARTING TRADING DASHBOARD")
|
|
logger.info(f"ACCESS WEB UI AT: http://{host}:{port}/")
|
|
logger.info("Real-time trading data and charts")
|
|
logger.info("AI model performance monitoring")
|
|
logger.info("Memory usage tracking")
|
|
logger.info("="*60)
|
|
|
|
# Run the app (updated API for newer Dash versions)
|
|
self.app.run(
|
|
host=host,
|
|
port=port,
|
|
debug=debug,
|
|
use_reloader=False, # Disable reloader to avoid conflicts
|
|
threaded=True # Enable threading for better performance
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error running dashboard: {e}")
|
|
raise
|
|
|
|
# Convenience function for integration
|
|
def create_dashboard(data_provider: DataProvider = None, orchestrator: TradingOrchestrator = None) -> TradingDashboard:
|
|
"""Create and return a trading dashboard instance"""
|
|
return TradingDashboard(data_provider, orchestrator) |