3.1 - 3.3 Add main Dash application for Crypto Trading Bot Dashboard
- Introduced `app.py` as the main entry point for the dashboard, providing real-time visualization and bot management interface. - Implemented layout components including header, navigation tabs, and content areas for market data, bot management, performance analytics, and system health. - Added callbacks for dynamic updates of market data charts and statistics, ensuring real-time interaction. - Created reusable UI components in `components` directory for modularity and maintainability. - Enhanced database operations for fetching market data and checking data availability. - Updated `main.py` to start the dashboard application with improved user instructions and error handling. - Documented components and functions for clarity and future reference.
This commit is contained in:
Vasily.onl
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358
app.py
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358
app.py
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#!/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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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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def create_app():
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"""Create and configure the Dash application."""
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# Initialize Dash app
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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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return app
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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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# Create the app instance
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app = create_app()
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# Tab switching callback
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@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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@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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@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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@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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@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()
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29
components/__init__.py
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components/__init__.py
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"""
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Dashboard UI Components Package
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This package contains reusable UI components for the Crypto Trading Bot Dashboard.
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Components are designed to be modular and can be composed to create complex layouts.
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"""
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from pathlib import Path
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# Package metadata
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__version__ = "0.1.0"
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__package_name__ = "components"
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# Make components directory available
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COMPONENTS_DIR = Path(__file__).parent
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# Component registry for future component discovery
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AVAILABLE_COMPONENTS = [
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"dashboard", # Main dashboard layout components
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"charts", # Chart and visualization components
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]
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def get_component_path(component_name: str) -> Path:
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"""Get the file path for a specific component."""
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return COMPONENTS_DIR / f"{component_name}.py"
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def list_components() -> list:
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"""List all available components."""
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return AVAILABLE_COMPONENTS.copy()
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components/charts.py
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components/charts.py
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"""
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Chart and Visualization Components
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This module provides chart components for market data visualization,
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including candlestick charts, technical indicators, and real-time updates.
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"""
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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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from datetime import datetime, timedelta, timezone
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from typing import List, Dict, Any, Optional
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from decimal import Decimal
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from database.operations import get_database_operations, DatabaseOperationError
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from utils.logger import get_logger
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# Initialize logger
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logger = get_logger("charts_component")
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def fetch_market_data(symbol: str, timeframe: str,
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days_back: int = 7, exchange: str = "okx") -> List[Dict[str, Any]]:
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"""
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Fetch market data from the database for chart display.
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Args:
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symbol: Trading pair (e.g., 'BTC-USDT')
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timeframe: Timeframe (e.g., '1h', '1d')
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days_back: Number of days to look back
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exchange: Exchange name
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Returns:
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List of candle data dictionaries
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"""
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try:
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db = get_database_operations(logger)
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# Calculate time range
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end_time = datetime.now(timezone.utc)
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start_time = end_time - timedelta(days=days_back)
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# Fetch candles from database using the proper API
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candles = db.market_data.get_candles(
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symbol=symbol,
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timeframe=timeframe,
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start_time=start_time,
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end_time=end_time,
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exchange=exchange
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)
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logger.debug(f"Fetched {len(candles)} candles for {symbol} {timeframe}")
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return candles
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except DatabaseOperationError as e:
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logger.error(f"Database error fetching market data: {e}")
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return []
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except Exception as e:
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logger.error(f"Unexpected error fetching market data: {e}")
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return []
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def create_candlestick_chart(symbol: str, timeframe: str,
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candles: Optional[List[Dict[str, Any]]] = None) -> go.Figure:
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"""
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Create a candlestick chart with real market data.
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Args:
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symbol: Trading pair
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timeframe: Timeframe
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candles: Optional pre-fetched candle data
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Returns:
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Plotly Figure object
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"""
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try:
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# Fetch data if not provided
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if candles is None:
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candles = fetch_market_data(symbol, timeframe)
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# Handle empty data
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if not candles:
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logger.warning(f"No data available for {symbol} {timeframe}")
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return create_empty_chart(f"No data available for {symbol} {timeframe}")
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# Convert to DataFrame for easier manipulation
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df = pd.DataFrame(candles)
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# Ensure timestamp column is datetime
|
||||
df['timestamp'] = pd.to_datetime(df['timestamp'])
|
||||
|
||||
# Sort by timestamp
|
||||
df = df.sort_values('timestamp')
|
||||
|
||||
# Create candlestick chart
|
||||
fig = go.Figure(data=go.Candlestick(
|
||||
x=df['timestamp'],
|
||||
open=df['open'],
|
||||
high=df['high'],
|
||||
low=df['low'],
|
||||
close=df['close'],
|
||||
name=symbol,
|
||||
increasing_line_color='#26a69a',
|
||||
decreasing_line_color='#ef5350'
|
||||
))
|
||||
|
||||
# Update layout
|
||||
fig.update_layout(
|
||||
title=f"{symbol} - {timeframe} Chart",
|
||||
xaxis_title="Time",
|
||||
yaxis_title="Price (USDT)",
|
||||
template="plotly_white",
|
||||
showlegend=False,
|
||||
height=600,
|
||||
xaxis_rangeslider_visible=False,
|
||||
hovermode='x unified'
|
||||
)
|
||||
|
||||
# Add volume subplot if volume data exists
|
||||
if 'volume' in df.columns and df['volume'].sum() > 0:
|
||||
fig = create_candlestick_with_volume(df, symbol, timeframe)
|
||||
|
||||
logger.debug(f"Created candlestick chart for {symbol} {timeframe} with {len(df)} candles")
|
||||
return fig
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating candlestick chart for {symbol} {timeframe}: {e}")
|
||||
return create_error_chart(f"Error loading chart: {str(e)}")
|
||||
|
||||
|
||||
def create_candlestick_with_volume(df: pd.DataFrame, symbol: str, timeframe: str) -> go.Figure:
|
||||
"""
|
||||
Create a candlestick chart with volume subplot.
|
||||
|
||||
Args:
|
||||
df: DataFrame with OHLCV data
|
||||
symbol: Trading pair
|
||||
timeframe: Timeframe
|
||||
|
||||
Returns:
|
||||
Plotly Figure with candlestick and volume
|
||||
"""
|
||||
# Create subplots
|
||||
fig = make_subplots(
|
||||
rows=2, cols=1,
|
||||
shared_xaxes=True,
|
||||
vertical_spacing=0.03,
|
||||
subplot_titles=(f'{symbol} Price', 'Volume'),
|
||||
row_width=[0.7, 0.3]
|
||||
)
|
||||
|
||||
# Add candlestick chart
|
||||
fig.add_trace(
|
||||
go.Candlestick(
|
||||
x=df['timestamp'],
|
||||
open=df['open'],
|
||||
high=df['high'],
|
||||
low=df['low'],
|
||||
close=df['close'],
|
||||
name=symbol,
|
||||
increasing_line_color='#26a69a',
|
||||
decreasing_line_color='#ef5350'
|
||||
),
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
# Add volume bars
|
||||
colors = ['#26a69a' if close >= open else '#ef5350'
|
||||
for close, open in zip(df['close'], df['open'])]
|
||||
|
||||
fig.add_trace(
|
||||
go.Bar(
|
||||
x=df['timestamp'],
|
||||
y=df['volume'],
|
||||
name='Volume',
|
||||
marker_color=colors,
|
||||
opacity=0.7
|
||||
),
|
||||
row=2, col=1
|
||||
)
|
||||
|
||||
# Update layout
|
||||
fig.update_layout(
|
||||
title=f"{symbol} - {timeframe} Chart with Volume",
|
||||
template="plotly_white",
|
||||
showlegend=False,
|
||||
height=700,
|
||||
xaxis_rangeslider_visible=False,
|
||||
hovermode='x unified'
|
||||
)
|
||||
|
||||
# Update axes
|
||||
fig.update_yaxes(title_text="Price (USDT)", row=1, col=1)
|
||||
fig.update_yaxes(title_text="Volume", row=2, col=1)
|
||||
fig.update_xaxes(title_text="Time", row=2, col=1)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def create_empty_chart(message: str = "No data available") -> go.Figure:
|
||||
"""
|
||||
Create an empty chart with a message.
|
||||
|
||||
Args:
|
||||
message: Message to display
|
||||
|
||||
Returns:
|
||||
Empty Plotly Figure
|
||||
"""
|
||||
fig = go.Figure()
|
||||
|
||||
fig.add_annotation(
|
||||
text=message,
|
||||
xref="paper", yref="paper",
|
||||
x=0.5, y=0.5,
|
||||
xanchor='center', yanchor='middle',
|
||||
showarrow=False,
|
||||
font=dict(size=16, color="#7f8c8d")
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
template="plotly_white",
|
||||
height=600,
|
||||
showlegend=False,
|
||||
xaxis=dict(visible=False),
|
||||
yaxis=dict(visible=False)
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def create_error_chart(error_message: str) -> go.Figure:
|
||||
"""
|
||||
Create an error chart with error message.
|
||||
|
||||
Args:
|
||||
error_message: Error message to display
|
||||
|
||||
Returns:
|
||||
Error Plotly Figure
|
||||
"""
|
||||
fig = go.Figure()
|
||||
|
||||
fig.add_annotation(
|
||||
text=f"⚠️ {error_message}",
|
||||
xref="paper", yref="paper",
|
||||
x=0.5, y=0.5,
|
||||
xanchor='center', yanchor='middle',
|
||||
showarrow=False,
|
||||
font=dict(size=16, color="#e74c3c")
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
template="plotly_white",
|
||||
height=600,
|
||||
showlegend=False,
|
||||
xaxis=dict(visible=False),
|
||||
yaxis=dict(visible=False)
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def get_market_statistics(symbol: str, timeframe: str = "1h") -> Dict[str, str]:
|
||||
"""
|
||||
Calculate market statistics from recent data.
|
||||
|
||||
Args:
|
||||
symbol: Trading pair
|
||||
timeframe: Timeframe for calculations
|
||||
|
||||
Returns:
|
||||
Dictionary of market statistics
|
||||
"""
|
||||
try:
|
||||
# Fetch recent data for statistics
|
||||
candles = fetch_market_data(symbol, timeframe, days_back=1)
|
||||
|
||||
if not candles:
|
||||
return {
|
||||
'Price': 'N/A',
|
||||
'24h Change': 'N/A',
|
||||
'24h Volume': 'N/A',
|
||||
'High 24h': 'N/A',
|
||||
'Low 24h': 'N/A'
|
||||
}
|
||||
|
||||
# Convert to DataFrame
|
||||
df = pd.DataFrame(candles)
|
||||
|
||||
# Get latest and 24h ago prices
|
||||
latest_candle = df.iloc[-1]
|
||||
current_price = float(latest_candle['close'])
|
||||
|
||||
# Calculate 24h change
|
||||
if len(df) > 1:
|
||||
price_24h_ago = float(df.iloc[0]['open'])
|
||||
change_24h = current_price - price_24h_ago
|
||||
change_percent = (change_24h / price_24h_ago) * 100
|
||||
else:
|
||||
change_24h = 0
|
||||
change_percent = 0
|
||||
|
||||
# Calculate volume and high/low
|
||||
total_volume = df['volume'].sum()
|
||||
high_24h = df['high'].max()
|
||||
low_24h = df['low'].min()
|
||||
|
||||
# Format statistics
|
||||
return {
|
||||
'Price': f"${current_price:,.2f}",
|
||||
'24h Change': f"{'+' if change_24h >= 0 else ''}{change_percent:.2f}%",
|
||||
'24h Volume': f"{total_volume:,.2f}",
|
||||
'High 24h': f"${float(high_24h):,.2f}",
|
||||
'Low 24h': f"${float(low_24h):,.2f}"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating market statistics for {symbol}: {e}")
|
||||
return {
|
||||
'Price': 'Error',
|
||||
'24h Change': 'Error',
|
||||
'24h Volume': 'Error',
|
||||
'High 24h': 'Error',
|
||||
'Low 24h': 'Error'
|
||||
}
|
||||
|
||||
|
||||
def check_data_availability(symbol: str, timeframe: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Check data availability for a symbol and timeframe.
|
||||
|
||||
Args:
|
||||
symbol: Trading pair
|
||||
timeframe: Timeframe
|
||||
|
||||
Returns:
|
||||
Dictionary with data availability information
|
||||
"""
|
||||
try:
|
||||
db = get_database_operations(logger)
|
||||
|
||||
# Get latest candle using the proper API
|
||||
latest_candle = db.market_data.get_latest_candle(symbol, timeframe)
|
||||
|
||||
if latest_candle:
|
||||
latest_time = latest_candle['timestamp']
|
||||
time_diff = datetime.now(timezone.utc) - latest_time.replace(tzinfo=timezone.utc)
|
||||
|
||||
return {
|
||||
'has_data': True,
|
||||
'latest_timestamp': latest_time,
|
||||
'time_since_last': time_diff,
|
||||
'is_recent': time_diff < timedelta(hours=1),
|
||||
'message': f"Latest data: {latest_time.strftime('%Y-%m-%d %H:%M:%S UTC')}"
|
||||
}
|
||||
else:
|
||||
return {
|
||||
'has_data': False,
|
||||
'latest_timestamp': None,
|
||||
'time_since_last': None,
|
||||
'is_recent': False,
|
||||
'message': f"No data available for {symbol} {timeframe}"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error checking data availability for {symbol} {timeframe}: {e}")
|
||||
return {
|
||||
'has_data': False,
|
||||
'latest_timestamp': None,
|
||||
'time_since_last': None,
|
||||
'is_recent': False,
|
||||
'message': f"Error checking data: {str(e)}"
|
||||
}
|
||||
|
||||
|
||||
def create_data_status_indicator(symbol: str, timeframe: str) -> str:
|
||||
"""
|
||||
Create a data status indicator for the dashboard.
|
||||
|
||||
Args:
|
||||
symbol: Trading pair
|
||||
timeframe: Timeframe
|
||||
|
||||
Returns:
|
||||
HTML string for status indicator
|
||||
"""
|
||||
status = check_data_availability(symbol, timeframe)
|
||||
|
||||
if status['has_data']:
|
||||
if status['is_recent']:
|
||||
icon = "🟢"
|
||||
color = "#27ae60"
|
||||
status_text = "Real-time Data"
|
||||
else:
|
||||
icon = "🟡"
|
||||
color = "#f39c12"
|
||||
status_text = "Delayed Data"
|
||||
else:
|
||||
icon = "🔴"
|
||||
color = "#e74c3c"
|
||||
status_text = "No Data"
|
||||
|
||||
return f'<span style="color: {color}; font-weight: bold;">{icon} {status_text}</span><br><small>{status["message"]}</small>'
|
||||
|
||||
|
||||
def get_supported_symbols() -> List[str]:
|
||||
"""
|
||||
Get list of symbols that have data in the database.
|
||||
|
||||
Returns:
|
||||
List of available trading pairs
|
||||
"""
|
||||
try:
|
||||
db = get_database_operations(logger)
|
||||
|
||||
with db.market_data.get_session() as session:
|
||||
# Query distinct symbols from market_data table
|
||||
from sqlalchemy import text
|
||||
result = session.execute(text("SELECT DISTINCT symbol FROM market_data ORDER BY symbol"))
|
||||
symbols = [row[0] for row in result]
|
||||
|
||||
logger.debug(f"Found {len(symbols)} symbols in database: {symbols}")
|
||||
return symbols
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching supported symbols: {e}")
|
||||
# Return default symbols if database query fails
|
||||
return ['BTC-USDT', 'ETH-USDT', 'LTC-USDT']
|
||||
|
||||
|
||||
def get_supported_timeframes() -> List[str]:
|
||||
"""
|
||||
Get list of timeframes that have data in the database.
|
||||
|
||||
Returns:
|
||||
List of available timeframes
|
||||
"""
|
||||
try:
|
||||
db = get_database_operations(logger)
|
||||
|
||||
with db.market_data.get_session() as session:
|
||||
# Query distinct timeframes from market_data table
|
||||
from sqlalchemy import text
|
||||
result = session.execute(text("SELECT DISTINCT timeframe FROM market_data ORDER BY timeframe"))
|
||||
timeframes = [row[0] for row in result]
|
||||
|
||||
logger.debug(f"Found {len(timeframes)} timeframes in database: {timeframes}")
|
||||
return timeframes
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching supported timeframes: {e}")
|
||||
# Return default timeframes if database query fails
|
||||
return ['1m', '5m', '15m', '1h', '4h', '1d']
|
||||
323
components/dashboard.py
Normal file
323
components/dashboard.py
Normal file
@ -0,0 +1,323 @@
|
||||
"""
|
||||
Dashboard Layout Components
|
||||
|
||||
This module contains reusable layout components for the main dashboard interface.
|
||||
These components handle the overall structure and navigation of the dashboard.
|
||||
"""
|
||||
|
||||
from dash import html, dcc
|
||||
from typing import List, Dict, Any, Optional
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def create_header(title: str = "Crypto Trading Bot Dashboard",
|
||||
subtitle: str = "Real-time monitoring and bot management") -> html.Div:
|
||||
"""
|
||||
Create the main dashboard header component.
|
||||
|
||||
Args:
|
||||
title: Main title text
|
||||
subtitle: Subtitle text
|
||||
|
||||
Returns:
|
||||
Dash HTML component for the header
|
||||
"""
|
||||
return html.Div([
|
||||
html.H1(f"🚀 {title}",
|
||||
style={'margin': '0', 'color': '#2c3e50', 'font-size': '28px'}),
|
||||
html.P(subtitle,
|
||||
style={'margin': '5px 0 0 0', 'color': '#7f8c8d', 'font-size': '14px'})
|
||||
], style={
|
||||
'padding': '20px',
|
||||
'background-color': '#ecf0f1',
|
||||
'border-bottom': '2px solid #bdc3c7',
|
||||
'box-shadow': '0 2px 4px rgba(0,0,0,0.1)'
|
||||
})
|
||||
|
||||
|
||||
def create_navigation_tabs(active_tab: str = 'market-data') -> dcc.Tabs:
|
||||
"""
|
||||
Create the main navigation tabs component.
|
||||
|
||||
Args:
|
||||
active_tab: Default active tab
|
||||
|
||||
Returns:
|
||||
Dash Tabs component
|
||||
"""
|
||||
tab_style = {
|
||||
'borderBottom': '1px solid #d6d6d6',
|
||||
'padding': '6px',
|
||||
'fontWeight': 'bold'
|
||||
}
|
||||
|
||||
tab_selected_style = {
|
||||
'borderTop': '1px solid #d6d6d6',
|
||||
'borderBottom': '1px solid #d6d6d6',
|
||||
'backgroundColor': '#119DFF',
|
||||
'color': 'white',
|
||||
'padding': '6px'
|
||||
}
|
||||
|
||||
return dcc.Tabs(
|
||||
id="main-tabs",
|
||||
value=active_tab,
|
||||
children=[
|
||||
dcc.Tab(
|
||||
label='📊 Market Data',
|
||||
value='market-data',
|
||||
style=tab_style,
|
||||
selected_style=tab_selected_style
|
||||
),
|
||||
dcc.Tab(
|
||||
label='🤖 Bot Management',
|
||||
value='bot-management',
|
||||
style=tab_style,
|
||||
selected_style=tab_selected_style
|
||||
),
|
||||
dcc.Tab(
|
||||
label='📈 Performance',
|
||||
value='performance',
|
||||
style=tab_style,
|
||||
selected_style=tab_selected_style
|
||||
),
|
||||
dcc.Tab(
|
||||
label='⚙️ System Health',
|
||||
value='system-health',
|
||||
style=tab_style,
|
||||
selected_style=tab_selected_style
|
||||
),
|
||||
],
|
||||
style={'margin': '10px 20px'}
|
||||
)
|
||||
|
||||
|
||||
def create_content_container(content_id: str = 'tab-content') -> html.Div:
|
||||
"""
|
||||
Create the main content container.
|
||||
|
||||
Args:
|
||||
content_id: HTML element ID for the content area
|
||||
|
||||
Returns:
|
||||
Dash HTML component for content container
|
||||
"""
|
||||
return html.Div(
|
||||
id=content_id,
|
||||
style={
|
||||
'padding': '20px',
|
||||
'min-height': '600px',
|
||||
'background-color': '#ffffff'
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def create_status_indicator(status: str, message: str,
|
||||
timestamp: Optional[datetime] = None) -> html.Div:
|
||||
"""
|
||||
Create a status indicator component.
|
||||
|
||||
Args:
|
||||
status: Status type ('connected', 'error', 'warning', 'info')
|
||||
message: Status message
|
||||
timestamp: Optional timestamp for the status
|
||||
|
||||
Returns:
|
||||
Dash HTML component for status indicator
|
||||
"""
|
||||
status_colors = {
|
||||
'connected': '#27ae60',
|
||||
'error': '#e74c3c',
|
||||
'warning': '#f39c12',
|
||||
'info': '#3498db'
|
||||
}
|
||||
|
||||
status_icons = {
|
||||
'connected': '🟢',
|
||||
'error': '🔴',
|
||||
'warning': '🟡',
|
||||
'info': '🔵'
|
||||
}
|
||||
|
||||
color = status_colors.get(status, '#7f8c8d')
|
||||
icon = status_icons.get(status, '⚪')
|
||||
|
||||
components = [
|
||||
html.Span(f"{icon} {message}",
|
||||
style={'color': color, 'font-weight': 'bold'})
|
||||
]
|
||||
|
||||
if timestamp:
|
||||
components.append(
|
||||
html.P(f"Last updated: {timestamp.strftime('%H:%M:%S')}",
|
||||
style={'margin': '5px 0', 'color': '#7f8c8d', 'font-size': '12px'})
|
||||
)
|
||||
|
||||
return html.Div(components)
|
||||
|
||||
|
||||
def create_card(title: str, content: Any,
|
||||
card_id: Optional[str] = None) -> html.Div:
|
||||
"""
|
||||
Create a card component for organizing content.
|
||||
|
||||
Args:
|
||||
title: Card title
|
||||
content: Card content (can be any Dash component)
|
||||
card_id: Optional HTML element ID
|
||||
|
||||
Returns:
|
||||
Dash HTML component for the card
|
||||
"""
|
||||
return html.Div([
|
||||
html.H3(title, style={
|
||||
'margin': '0 0 15px 0',
|
||||
'color': '#2c3e50',
|
||||
'border-bottom': '2px solid #ecf0f1',
|
||||
'padding-bottom': '10px'
|
||||
}),
|
||||
content
|
||||
], style={
|
||||
'border': '1px solid #ddd',
|
||||
'border-radius': '8px',
|
||||
'padding': '20px',
|
||||
'margin': '10px 0',
|
||||
'background-color': '#ffffff',
|
||||
'box-shadow': '0 2px 4px rgba(0,0,0,0.1)'
|
||||
}, id=card_id)
|
||||
|
||||
|
||||
def create_metric_display(metrics: Dict[str, str]) -> html.Div:
|
||||
"""
|
||||
Create a metrics display component.
|
||||
|
||||
Args:
|
||||
metrics: Dictionary of metric names and values
|
||||
|
||||
Returns:
|
||||
Dash HTML component for metrics display
|
||||
"""
|
||||
metric_components = []
|
||||
|
||||
for key, value in metrics.items():
|
||||
# Color coding for percentage changes
|
||||
color = '#27ae60' if '+' in str(value) else '#e74c3c' if '-' in str(value) else '#2c3e50'
|
||||
|
||||
metric_components.append(
|
||||
html.Div([
|
||||
html.Strong(f"{key}: ", style={'color': '#2c3e50'}),
|
||||
html.Span(str(value), style={'color': color})
|
||||
], style={
|
||||
'margin': '8px 0',
|
||||
'padding': '5px',
|
||||
'background-color': '#f8f9fa',
|
||||
'border-radius': '4px'
|
||||
})
|
||||
)
|
||||
|
||||
return html.Div(metric_components, style={
|
||||
'display': 'grid',
|
||||
'grid-template-columns': 'repeat(auto-fit, minmax(200px, 1fr))',
|
||||
'gap': '10px'
|
||||
})
|
||||
|
||||
|
||||
def create_selector_group(selectors: List[Dict[str, Any]]) -> html.Div:
|
||||
"""
|
||||
Create a group of selector components (dropdowns, etc.).
|
||||
|
||||
Args:
|
||||
selectors: List of selector configurations
|
||||
|
||||
Returns:
|
||||
Dash HTML component for selector group
|
||||
"""
|
||||
selector_components = []
|
||||
|
||||
for selector in selectors:
|
||||
selector_div = html.Div([
|
||||
html.Label(
|
||||
selector.get('label', ''),
|
||||
style={'font-weight': 'bold', 'margin-bottom': '5px', 'display': 'block'}
|
||||
),
|
||||
dcc.Dropdown(
|
||||
id=selector.get('id'),
|
||||
options=selector.get('options', []),
|
||||
value=selector.get('value'),
|
||||
style={'margin-bottom': '15px'}
|
||||
)
|
||||
], style={'width': '250px', 'margin': '10px 20px 10px 0', 'display': 'inline-block'})
|
||||
|
||||
selector_components.append(selector_div)
|
||||
|
||||
return html.Div(selector_components, style={'margin': '20px 0'})
|
||||
|
||||
|
||||
def create_loading_component(component_id: str, message: str = "Loading...") -> html.Div:
|
||||
"""
|
||||
Create a loading component for async operations.
|
||||
|
||||
Args:
|
||||
component_id: ID for the component that will replace this loading screen
|
||||
message: Loading message
|
||||
|
||||
Returns:
|
||||
Dash HTML component for loading screen
|
||||
"""
|
||||
return html.Div([
|
||||
html.Div([
|
||||
html.Div(className="loading-spinner", style={
|
||||
'border': '4px solid #f3f3f3',
|
||||
'border-top': '4px solid #3498db',
|
||||
'border-radius': '50%',
|
||||
'width': '40px',
|
||||
'height': '40px',
|
||||
'animation': 'spin 2s linear infinite',
|
||||
'margin': '0 auto 20px auto'
|
||||
}),
|
||||
html.P(message, style={'text-align': 'center', 'color': '#7f8c8d'})
|
||||
], style={
|
||||
'display': 'flex',
|
||||
'flex-direction': 'column',
|
||||
'align-items': 'center',
|
||||
'justify-content': 'center',
|
||||
'height': '200px'
|
||||
})
|
||||
], id=component_id)
|
||||
|
||||
|
||||
def create_placeholder_content(title: str, description: str,
|
||||
phase: str = "future implementation") -> html.Div:
|
||||
"""
|
||||
Create placeholder content for features not yet implemented.
|
||||
|
||||
Args:
|
||||
title: Section title
|
||||
description: Description of what will be implemented
|
||||
phase: Implementation phase information
|
||||
|
||||
Returns:
|
||||
Dash HTML component for placeholder content
|
||||
"""
|
||||
return html.Div([
|
||||
html.H2(title, style={'color': '#2c3e50'}),
|
||||
html.Div([
|
||||
html.P(description, style={'color': '#7f8c8d', 'font-size': '16px'}),
|
||||
html.P(f"🚧 Planned for {phase}",
|
||||
style={'color': '#f39c12', 'font-weight': 'bold', 'font-style': 'italic'})
|
||||
], style={
|
||||
'background-color': '#f8f9fa',
|
||||
'padding': '20px',
|
||||
'border-radius': '8px',
|
||||
'border-left': '4px solid #f39c12'
|
||||
})
|
||||
])
|
||||
|
||||
|
||||
# CSS Styles for animation (to be included in assets or inline styles)
|
||||
LOADING_CSS = """
|
||||
@keyframes spin {
|
||||
0% { transform: rotate(0deg); }
|
||||
100% { transform: rotate(360deg); }
|
||||
}
|
||||
"""
|
||||
@ -169,7 +169,7 @@ class MarketDataRepository(BaseRepository):
|
||||
query = text("""
|
||||
SELECT exchange, symbol, timeframe, timestamp,
|
||||
open, high, low, close, volume, trades_count,
|
||||
created_at, updated_at
|
||||
created_at
|
||||
FROM market_data
|
||||
WHERE exchange = :exchange
|
||||
AND symbol = :symbol
|
||||
@ -200,15 +200,14 @@ class MarketDataRepository(BaseRepository):
|
||||
'close': row.close,
|
||||
'volume': row.volume,
|
||||
'trades_count': row.trades_count,
|
||||
'created_at': row.created_at,
|
||||
'updated_at': row.updated_at
|
||||
'created_at': row.created_at
|
||||
})
|
||||
|
||||
self.log_info(f"Retrieved {len(candles)} candles for {symbol} {timeframe}")
|
||||
self.log_debug(f"Retrieved {len(candles)} candles for {symbol} {timeframe}")
|
||||
return candles
|
||||
|
||||
except Exception as e:
|
||||
self.log_error(f"Error retrieving candles for {symbol} {timeframe}: {e}")
|
||||
self.log_error(f"Error retrieving candles: {e}")
|
||||
raise DatabaseOperationError(f"Failed to retrieve candles: {e}")
|
||||
|
||||
def get_latest_candle(self, symbol: str, timeframe: str, exchange: str = "okx") -> Optional[Dict[str, Any]]:
|
||||
@ -228,7 +227,7 @@ class MarketDataRepository(BaseRepository):
|
||||
query = text("""
|
||||
SELECT exchange, symbol, timeframe, timestamp,
|
||||
open, high, low, close, volume, trades_count,
|
||||
created_at, updated_at
|
||||
created_at
|
||||
FROM market_data
|
||||
WHERE exchange = :exchange
|
||||
AND symbol = :symbol
|
||||
@ -256,8 +255,7 @@ class MarketDataRepository(BaseRepository):
|
||||
'close': row.close,
|
||||
'volume': row.volume,
|
||||
'trades_count': row.trades_count,
|
||||
'created_at': row.created_at,
|
||||
'updated_at': row.updated_at
|
||||
'created_at': row.created_at
|
||||
}
|
||||
return None
|
||||
|
||||
|
||||
26
main.py
26
main.py
@ -23,23 +23,29 @@ def main():
|
||||
|
||||
if app.environment == "development":
|
||||
print("\n🔧 Running in development mode")
|
||||
print("To start the full application:")
|
||||
print("1. Run: python scripts/dev.py setup")
|
||||
print("2. Run: python scripts/dev.py start")
|
||||
print("3. Update .env with your OKX API credentials")
|
||||
print("4. Run: uv run python tests/test_setup.py")
|
||||
print("Dashboard features available:")
|
||||
print("✅ Basic Dash application framework")
|
||||
print("✅ Real-time price charts (sample data)")
|
||||
print("✅ System health monitoring")
|
||||
print("🚧 Real data connection (coming in task 3.7)")
|
||||
|
||||
# TODO: Start the Dash application when ready
|
||||
# from app import create_app
|
||||
# app = create_app()
|
||||
# app.run(host=dashboard.host, port=dashboard.port, debug=dashboard.debug)
|
||||
# Start the Dash application
|
||||
print(f"\n🌐 Starting dashboard at: http://{dashboard.host}:{dashboard.port}")
|
||||
print("Press Ctrl+C to stop the application")
|
||||
|
||||
print(f"\n📝 Next: Implement Phase 1.0 - Database Infrastructure Setup")
|
||||
from app import main as app_main
|
||||
app_main()
|
||||
|
||||
except ImportError as e:
|
||||
print(f"❌ Failed to import modules: {e}")
|
||||
print("Run: uv sync")
|
||||
sys.exit(1)
|
||||
except KeyboardInterrupt:
|
||||
print("\n\n👋 Dashboard stopped by user")
|
||||
sys.exit(0)
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to start dashboard: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -77,9 +77,9 @@
|
||||
- [x] 2.9 Unit test data collection and aggregation logic
|
||||
|
||||
- [ ] 3.0 Basic Dashboard for Data Visualization and Analysis
|
||||
- [ ] 3.1 Setup Dash application framework with Mantine UI components
|
||||
- [ ] 3.2 Create basic layout and navigation structure
|
||||
- [ ] 3.3 Implement real-time OHLCV price charts with Plotly (candlestick charts)
|
||||
- [x] 3.1 Setup Dash application framework with Mantine UI components
|
||||
- [x] 3.2 Create basic layout and navigation structure
|
||||
- [x] 3.3 Implement real-time OHLCV price charts with Plotly (candlestick charts)
|
||||
- [ ] 3.4 Add technical indicators overlay on price charts (SMA, EMA, RSI, MACD)
|
||||
- [ ] 3.5 Create market data monitoring dashboard (real-time data feed status)
|
||||
- [ ] 3.6 Build simple data analysis tools (volume analysis, price movement statistics)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user