a969defe1f
Implement modular chart layers and error handling for Crypto Trading Bot Dashboard - Introduced a comprehensive chart layer system in `components/charts/layers/` to support various technical indicators and subplots. - Added base layer components including `BaseLayer`, `CandlestickLayer`, and `VolumeLayer` for flexible chart rendering. - Implemented overlay indicators such as `SMALayer`, `EMALayer`, and `BollingerBandsLayer` with robust error handling. - Created subplot layers for indicators like `RSILayer` and `MACDLayer`, enhancing visualization capabilities. - Developed a `MarketDataIntegrator` for seamless data fetching and validation, improving data quality assurance. - Enhanced error handling utilities in `components/charts/error_handling.py` to manage insufficient data scenarios effectively. - Updated documentation to reflect the new chart layer architecture and usage guidelines. - Added unit tests for all chart layer components to ensure functionality and reliability.
358 lines
12 KiB
Python
358 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Main Dash application for the Crypto Trading Bot Dashboard.
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Provides real-time visualization and bot management interface.
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"""
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import sys
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from pathlib import Path
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# Add project root to path
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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# Suppress SQLAlchemy logging to reduce verbosity
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import logging
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logging.getLogger('sqlalchemy').setLevel(logging.WARNING)
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logging.getLogger('sqlalchemy.engine').setLevel(logging.WARNING)
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logging.getLogger('sqlalchemy.pool').setLevel(logging.WARNING)
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logging.getLogger('sqlalchemy.dialects').setLevel(logging.WARNING)
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logging.getLogger('sqlalchemy.orm').setLevel(logging.WARNING)
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import dash
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from dash import dcc, html, Input, Output, callback
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import plotly.graph_objects as go
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from datetime import datetime, timedelta
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import pandas as pd
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# Import project modules
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from config.settings import app as app_settings, dashboard as dashboard_settings
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from utils.logger import get_logger
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from database.connection import DatabaseManager
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from components.charts import (
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create_candlestick_chart, get_market_statistics,
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get_supported_symbols, get_supported_timeframes,
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create_data_status_indicator, check_data_availability,
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create_error_chart
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)
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# Initialize logger
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logger = get_logger("dashboard_app")
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# Create the app instance at module level
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app = dash.Dash(
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__name__,
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title="Crypto Trading Bot Dashboard",
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update_title="Loading...",
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suppress_callback_exceptions=True
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)
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# Configure app
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app.server.secret_key = "crypto-bot-dashboard-secret-key-2024"
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logger.info("Initializing Crypto Trading Bot Dashboard")
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# Define basic layout
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app.layout = html.Div([
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# Header
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html.Div([
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html.H1("🚀 Crypto Trading Bot Dashboard",
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style={'margin': '0', 'color': '#2c3e50'}),
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html.P("Real-time monitoring and bot management",
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style={'margin': '5px 0 0 0', 'color': '#7f8c8d'})
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], style={
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'padding': '20px',
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'background-color': '#ecf0f1',
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'border-bottom': '2px solid #bdc3c7'
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}),
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# Navigation tabs
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dcc.Tabs(id="main-tabs", value='market-data', children=[
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dcc.Tab(label='📊 Market Data', value='market-data'),
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dcc.Tab(label='🤖 Bot Management', value='bot-management'),
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dcc.Tab(label='📈 Performance', value='performance'),
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dcc.Tab(label='⚙️ System Health', value='system-health'),
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], style={'margin': '10px 20px'}),
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# Main content area
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html.Div(id='tab-content', style={'padding': '20px'}),
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# Auto-refresh interval for real-time updates
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dcc.Interval(
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id='interval-component',
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interval=5000, # Update every 5 seconds
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n_intervals=0
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),
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# Store components for data sharing between callbacks
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dcc.Store(id='market-data-store'),
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dcc.Store(id='bot-status-store'),
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])
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def get_market_data_layout():
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"""Create the market data visualization layout."""
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# Get available symbols and timeframes from database
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symbols = get_supported_symbols()
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timeframes = get_supported_timeframes()
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# Create dropdown options
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symbol_options = [{'label': symbol, 'value': symbol} for symbol in symbols]
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timeframe_options = [
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{'label': '1 Minute', 'value': '1m'},
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{'label': '5 Minutes', 'value': '5m'},
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{'label': '15 Minutes', 'value': '15m'},
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{'label': '1 Hour', 'value': '1h'},
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{'label': '4 Hours', 'value': '4h'},
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{'label': '1 Day', 'value': '1d'},
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]
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# Filter timeframe options to only show those available in database
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available_timeframes = [tf for tf in ['1m', '5m', '15m', '1h', '4h', '1d'] if tf in timeframes]
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if not available_timeframes:
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available_timeframes = ['1h'] # Default fallback
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timeframe_options = [opt for opt in timeframe_options if opt['value'] in available_timeframes]
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return html.Div([
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html.H2("📊 Real-time Market Data", style={'color': '#2c3e50'}),
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# Symbol selector
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html.Div([
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html.Label("Select Trading Pair:", style={'font-weight': 'bold'}),
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dcc.Dropdown(
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id='symbol-dropdown',
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options=symbol_options,
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value=symbols[0] if symbols else 'BTC-USDT',
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style={'margin': '10px 0'}
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)
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], style={'width': '300px', 'margin': '20px 0'}),
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# Timeframe selector
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html.Div([
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html.Label("Timeframe:", style={'font-weight': 'bold'}),
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dcc.Dropdown(
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id='timeframe-dropdown',
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options=timeframe_options,
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value=available_timeframes[0] if available_timeframes else '1h',
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style={'margin': '10px 0'}
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)
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], style={'width': '300px', 'margin': '20px 0'}),
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# Price chart
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dcc.Graph(
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id='price-chart',
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style={'height': '600px', 'margin': '20px 0'},
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config={'displayModeBar': True, 'displaylogo': False}
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),
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# Market statistics
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html.Div(id='market-stats', style={'margin': '20px 0'}),
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# Data status indicator
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html.Div(id='data-status', style={'margin': '20px 0'})
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])
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def get_bot_management_layout():
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"""Create the bot management layout."""
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return html.Div([
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html.H2("🤖 Bot Management", style={'color': '#2c3e50'}),
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html.P("Bot management interface will be implemented in Phase 4.0"),
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# Placeholder for bot list
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html.Div([
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html.H3("Active Bots"),
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html.Div(id='bot-list', children=[
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html.P("No bots currently running", style={'color': '#7f8c8d'})
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])
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], style={'margin': '20px 0'})
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])
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def get_performance_layout():
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"""Create the performance monitoring layout."""
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return html.Div([
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html.H2("📈 Performance Analytics", style={'color': '#2c3e50'}),
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html.P("Performance analytics will be implemented in Phase 6.0"),
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# Placeholder for performance metrics
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html.Div([
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html.H3("Portfolio Performance"),
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html.P("Portfolio tracking coming soon", style={'color': '#7f8c8d'})
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], style={'margin': '20px 0'})
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])
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def get_system_health_layout():
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"""Create the system health monitoring layout."""
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return html.Div([
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html.H2("⚙️ System Health", style={'color': '#2c3e50'}),
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# Database status
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html.Div([
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html.H3("Database Status"),
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html.Div(id='database-status')
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], style={'margin': '20px 0'}),
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# Data collection status
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html.Div([
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html.H3("Data Collection Status"),
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html.Div(id='collection-status')
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], style={'margin': '20px 0'}),
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# Redis status
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html.Div([
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html.H3("Redis Status"),
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html.Div(id='redis-status')
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], style={'margin': '20px 0'})
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])
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# Tab switching callback
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@app.callback(
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Output('tab-content', 'children'),
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Input('main-tabs', 'value')
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)
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def render_tab_content(active_tab):
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"""Render content based on selected tab."""
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if active_tab == 'market-data':
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return get_market_data_layout()
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elif active_tab == 'bot-management':
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return get_bot_management_layout()
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elif active_tab == 'performance':
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return get_performance_layout()
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elif active_tab == 'system-health':
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return get_system_health_layout()
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else:
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return html.Div("Tab not found")
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# Market data chart callback
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@app.callback(
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Output('price-chart', 'figure'),
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[Input('symbol-dropdown', 'value'),
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Input('timeframe-dropdown', 'value'),
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Input('interval-component', 'n_intervals')]
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)
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def update_price_chart(symbol, timeframe, n_intervals):
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"""Update the price chart with latest market data."""
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try:
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# Use the real chart component instead of sample data
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fig = create_candlestick_chart(symbol, timeframe)
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logger.debug(f"Updated chart for {symbol} ({timeframe}) - interval {n_intervals}")
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return fig
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except Exception as e:
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logger.error(f"Error updating price chart: {e}")
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# Return error chart on failure
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return create_error_chart(f"Error loading chart: {str(e)}")
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# Market statistics callback
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@app.callback(
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Output('market-stats', 'children'),
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[Input('symbol-dropdown', 'value'),
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Input('interval-component', 'n_intervals')]
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)
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def update_market_stats(symbol, n_intervals):
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"""Update market statistics."""
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try:
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# Get real market statistics from database
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stats = get_market_statistics(symbol)
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return html.Div([
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html.H3("Market Statistics"),
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html.Div([
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html.Div([
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html.Strong(f"{key}: "),
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html.Span(value, style={'color': '#27ae60' if '+' in str(value) else '#e74c3c' if '-' in str(value) else '#2c3e50'})
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], style={'margin': '5px 0'}) for key, value in stats.items()
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])
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])
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except Exception as e:
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logger.error(f"Error updating market stats: {e}")
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return html.Div("Error loading market statistics")
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# System health callbacks
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@app.callback(
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Output('database-status', 'children'),
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Input('interval-component', 'n_intervals')
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)
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def update_database_status(n_intervals):
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"""Update database connection status."""
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try:
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db_manager = DatabaseManager()
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# Test database connection
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with db_manager.get_session() as session:
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# Simple query to test connection
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result = session.execute("SELECT 1").fetchone()
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if result:
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return html.Div([
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html.Span("🟢 Connected", style={'color': '#27ae60', 'font-weight': 'bold'}),
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html.P(f"Last checked: {datetime.now().strftime('%H:%M:%S')}",
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style={'margin': '5px 0', 'color': '#7f8c8d'})
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])
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else:
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return html.Div([
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html.Span("🔴 Connection Error", style={'color': '#e74c3c', 'font-weight': 'bold'})
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])
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except Exception as e:
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logger.error(f"Database status check failed: {e}")
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return html.Div([
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html.Span("🔴 Connection Failed", style={'color': '#e74c3c', 'font-weight': 'bold'}),
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html.P(f"Error: {str(e)}", style={'color': '#7f8c8d', 'font-size': '12px'})
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])
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@app.callback(
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Output('data-status', 'children'),
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[Input('symbol-dropdown', 'value'),
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Input('timeframe-dropdown', 'value'),
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Input('interval-component', 'n_intervals')]
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)
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def update_data_status(symbol, timeframe, n_intervals):
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"""Update data collection status."""
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try:
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# Check real data availability
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status = check_data_availability(symbol, timeframe)
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return html.Div([
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html.H3("Data Collection Status"),
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html.Div([
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html.Div(
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create_data_status_indicator(symbol, timeframe),
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style={'margin': '10px 0'}
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),
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html.P(f"Checking data for {symbol} {timeframe}",
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style={'color': '#7f8c8d', 'margin': '5px 0', 'font-style': 'italic'})
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], style={'background-color': '#f8f9fa', 'padding': '15px', 'border-radius': '5px'})
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])
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except Exception as e:
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logger.error(f"Error updating data status: {e}")
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return html.Div([
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html.H3("Data Collection Status"),
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html.Div([
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html.Span("🔴 Status Check Failed", style={'color': '#e74c3c', 'font-weight': 'bold'}),
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html.P(f"Error: {str(e)}", style={'color': '#7f8c8d', 'margin': '5px 0'})
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])
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])
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def main():
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"""Main function to run the dashboard."""
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try:
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logger.info("Starting Crypto Trading Bot Dashboard")
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logger.info(f"Dashboard will be available at: http://{dashboard_settings.host}:{dashboard_settings.port}")
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# Run the app
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app.run(
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host=dashboard_settings.host,
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port=dashboard_settings.port,
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debug=dashboard_settings.debug
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)
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except Exception as e:
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logger.error(f"Failed to start dashboard: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main() |