mirror of
https://github.com/B-Wear/QuantumEdge.git
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B-Wear
372 lines
14 KiB
Python
372 lines
14 KiB
Python
import dash
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from dash import dcc, html
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from dash.dependencies import Input, Output, State
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import pandas as pd
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import json
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import os
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from datetime import datetime
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import numpy as np
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from .code_guardian import create_guardian, CodeGuardian
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def load_backtest_results(results_dir):
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"""Load all backtest results from directory"""
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results = {}
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for file in os.listdir(results_dir):
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if file.startswith('backtest_results_') and file.endswith('.json'):
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with open(os.path.join(results_dir, file), 'r') as f:
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scenario_name = file.replace('backtest_results_', '').replace('.json', '')
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results[scenario_name] = json.load(f)
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return results
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def create_dashboard(results_dir='backtest_results'):
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"""Create and run the dashboard"""
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app = dash.Dash(__name__)
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# Load results
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results = load_backtest_results(results_dir)
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# Initialize Code Guardian
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guardian = create_guardian(os.path.dirname(os.path.dirname(__file__)))
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# Dashboard 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('AI Trading Bot - Backtest Results & System Health Dashboard',
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style={'textAlign': 'center', 'color': '#2c3e50', 'marginBottom': 30}),
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], style={'backgroundColor': '#ecf0f1', 'padding': '20px'}),
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# Tabs for different sections
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dcc.Tabs([
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# Backtest Results Tab
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dcc.Tab(label='Backtest Results', children=[
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# Scenario Selection
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html.Div([
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html.Label('Select Scenario:'),
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dcc.Dropdown(
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id='scenario-dropdown',
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options=[{'label': k, 'value': k} for k in results.keys()],
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value=list(results.keys())[0] if results else None,
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style={'width': '100%'}
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),
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], style={'margin': '20px'}),
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# Main Charts
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html.Div([
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# Trading Chart
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html.Div([
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dcc.Graph(id='trading-chart')
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], style={'width': '100%', 'marginBottom': '20px'}),
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# Performance Metrics Cards
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html.Div([
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html.Div([
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html.Div(id='metrics-cards'),
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], style={'display': 'flex', 'flexWrap': 'wrap', 'justifyContent': 'space-around'})
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], style={'marginBottom': '20px'}),
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# Trade Distribution Chart
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html.Div([
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dcc.Graph(id='trade-distribution')
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], style={'width': '100%', 'marginBottom': '20px'}),
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# Drawdown Chart
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html.Div([
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dcc.Graph(id='drawdown-chart')
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], style={'width': '100%'})
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], style={'padding': '20px'})
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]),
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# System Health Tab
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dcc.Tab(label='System Health', children=[
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html.Div([
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# Code Health Overview
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html.Div([
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html.H2('Code Health Overview', style={'textAlign': 'center'}),
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html.Div(id='code-health-cards', style={
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'display': 'flex',
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'flexWrap': 'wrap',
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'justifyContent': 'space-around',
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'margin': '20px'
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})
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]),
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# Code Issues Table
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html.Div([
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html.H3('Active Code Issues'),
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html.Div(id='code-issues-table')
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], style={'margin': '20px'}),
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# Performance Metrics
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html.Div([
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html.H3('Module Performance'),
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dcc.Graph(id='performance-chart')
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], style={'margin': '20px'}),
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# Auto-Fix Controls
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html.Div([
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html.H3('Issue Resolution'),
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html.Button('Auto-Fix Selected Issues', id='auto-fix-button'),
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html.Div(id='auto-fix-status')
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], style={'margin': '20px'})
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])
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]),
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# System Logs Tab
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dcc.Tab(label='System Logs', children=[
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html.Div([
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html.H2('System Logs'),
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dcc.Interval(id='log-update', interval=5000), # Update every 5 seconds
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html.Pre(id='log-content', style={
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'backgroundColor': '#2c3e50',
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'color': '#ecf0f1',
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'padding': '20px',
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'borderRadius': '5px',
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'height': '500px',
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'overflow': 'auto'
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})
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], style={'margin': '20px'})
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])
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])
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])
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@app.callback(
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[Output('trading-chart', 'figure'),
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Output('metrics-cards', 'children'),
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Output('trade-distribution', 'figure'),
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Output('drawdown-chart', 'figure')],
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[Input('scenario-dropdown', 'value')]
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)
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def update_charts(selected_scenario):
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if not selected_scenario or selected_scenario not in results:
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return {}, [], {}, {}
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result = results[selected_scenario]
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trades_df = pd.DataFrame(result['trades'])
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equity_df = pd.DataFrame(result['equity_curve'])
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metrics = result['performance_metrics']
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# 1. Trading Chart
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trading_fig = make_subplots(
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rows=2, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.03,
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subplot_titles=('Price and Trades', 'Equity Curve'),
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row_heights=[0.7, 0.3]
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)
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# Add price line
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trading_fig.add_trace(
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go.Scatter(
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x=trades_df['timestamp'],
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y=trades_df['price'],
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name='Price',
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line=dict(color='#2980b9')
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),
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row=1, col=1
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)
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# Add buy/sell markers
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for action, color in [('buy', '#27ae60'), ('sell', '#c0392b')]:
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mask = trades_df['action'] == action
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trading_fig.add_trace(
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go.Scatter(
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x=trades_df[mask]['timestamp'],
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y=trades_df[mask]['price'],
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mode='markers',
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name=action.capitalize(),
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marker=dict(color=color, size=10)
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),
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row=1, col=1
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)
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# Add equity curve
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trading_fig.add_trace(
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go.Scatter(
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x=equity_df['timestamp'],
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y=equity_df['equity'],
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name='Equity',
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line=dict(color='#8e44ad')
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),
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row=2, col=1
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)
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trading_fig.update_layout(
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title='Trading Activity and Equity Curve',
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height=800,
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template='plotly_white'
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)
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# 2. Metrics Cards
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cards = []
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metrics_style = {
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'width': '200px',
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'margin': '10px',
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'padding': '15px',
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'borderRadius': '5px',
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'boxShadow': '2px 2px 5px rgba(0,0,0,0.1)',
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'textAlign': 'center'
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}
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metrics_data = [
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('Total Return', f"{metrics['total_return']:.2%}", '#27ae60'),
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('Win Rate', f"{metrics['win_rate']:.2%}", '#2980b9'),
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('Sharpe Ratio', f"{metrics['sharpe_ratio']:.2f}", '#8e44ad'),
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('Max Drawdown', f"{metrics['max_drawdown']:.2%}", '#c0392b'),
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('Total Trades', str(metrics['total_trades']), '#2c3e50')
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]
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for label, value, color in metrics_data:
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cards.append(html.Div([
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html.H4(label, style={'color': '#7f8c8d'}),
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html.H3(value, style={'color': color})
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], style=metrics_style))
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# 3. Trade Distribution
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returns = pd.Series([float(x) for x in trades_df['price'].pct_change().dropna()])
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dist_fig = go.Figure()
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dist_fig.add_trace(go.Histogram(
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x=returns,
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nbinsx=30,
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name='Returns Distribution',
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marker_color='#3498db'
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))
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dist_fig.update_layout(
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title='Trade Returns Distribution',
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xaxis_title='Return',
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yaxis_title='Frequency',
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template='plotly_white'
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)
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# 4. Drawdown Chart
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equity_series = pd.Series([float(x) for x in equity_df['equity']])
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rolling_max = equity_series.expanding().max()
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drawdowns = (equity_series - rolling_max) / rolling_max
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dd_fig = go.Figure()
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dd_fig.add_trace(go.Scatter(
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x=equity_df['timestamp'],
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y=drawdowns,
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fill='tozeroy',
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name='Drawdown',
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line=dict(color='#e74c3c')
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))
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dd_fig.update_layout(
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title='Drawdown Over Time',
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xaxis_title='Date',
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yaxis_title='Drawdown',
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template='plotly_white'
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)
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return trading_fig, cards, dist_fig, dd_fig
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@app.callback(
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[Output('code-health-cards', 'children'),
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Output('code-issues-table', 'children'),
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Output('performance-chart', 'figure'),
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Output('auto-fix-status', 'children')],
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[Input('auto-fix-button', 'n_clicks')],
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[State('code-issues-table', 'selected_rows')]
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)
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def update_system_health(n_clicks, selected_rows):
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# Get guardian status
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status = guardian.get_status_report()
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# Create health cards
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health_cards = []
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health_metrics = [
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('Total Issues', status['total_issues'], '#e74c3c'),
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('Fixed Issues', status['fixed_issues'], '#27ae60'),
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('Active Issues', status['active_issues'], '#f39c12'),
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('Code Coverage', '85%', '#3498db') # Example metric
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]
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for label, value, color in health_metrics:
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health_cards.append(html.Div([
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html.H4(label),
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html.H3(str(value))
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], style={
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'width': '200px',
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'margin': '10px',
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'padding': '15px',
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'borderRadius': '5px',
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'boxShadow': '2px 2px 5px rgba(0,0,0,0.1)',
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'textAlign': 'center',
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'backgroundColor': color,
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'color': 'white'
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}))
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# Create issues table
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issues_table = html.Table([
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html.Thead(html.Tr([
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html.Th('File'),
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html.Th('Line'),
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html.Th('Type'),
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html.Th('Severity'),
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html.Th('Description'),
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html.Th('Status')
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])),
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html.Tbody([
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html.Tr([
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html.Td(issue.file_path),
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html.Td(issue.line_number),
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html.Td(issue.issue_type),
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html.Td(issue.severity),
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html.Td(issue.description),
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html.Td('Fixed' if issue.fixed else 'Active')
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]) for issue in guardian.issues
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])
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], style={'width': '100%'})
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# Create performance chart
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perf_data = status['performance_metrics']
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perf_fig = go.Figure()
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for module, metrics in perf_data.items():
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perf_fig.add_trace(go.Scatter(
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x=list(range(len(guardian.performance_metrics[module]))),
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y=guardian.performance_metrics[module],
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name=module,
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mode='lines+markers'
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))
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perf_fig.update_layout(
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title='Module Performance Over Time',
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xaxis_title='Execution Count',
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yaxis_title='Execution Time (s)',
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template='plotly_white'
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)
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# Handle auto-fix status
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fix_status = None
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if n_clicks and selected_rows:
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fixed_count = sum(1 for i in selected_rows if guardian.fix_issue(guardian.issues[i]))
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fix_status = f"Fixed {fixed_count} out of {len(selected_rows)} selected issues"
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return health_cards, issues_table, perf_fig, fix_status
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@app.callback(
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Output('log-content', 'children'),
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[Input('log-update', 'n_intervals')]
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)
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def update_logs(_):
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try:
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with open('analysis.log', 'r') as f:
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logs = f.readlines()
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return ''.join(logs[-100:]) # Show last 100 lines
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except Exception as e:
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return f"Error reading logs: {str(e)}"
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return app
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def run_dashboard(port=8050):
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"""Run the dashboard"""
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app = create_dashboard()
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app.run_server(debug=True, port=port)
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if __name__ == '__main__':
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run_dashboard() |