refactoring, predictions WIP
This commit is contained in:
Dobromir Popov
267
web/cnn_dashboard.py
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267
web/cnn_dashboard.py
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#!/usr/bin/env python3
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"""
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CNN Trading Dashboard - Web UI Layer
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This is a lightweight Dash application that provides the web interface
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for CNN pivot predictions. All business logic is handled by core modules.
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"""
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import logging
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import sys
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import os
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from datetime import datetime
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# Add core modules to path
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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import dash
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from dash import dcc, html, Input, Output, callback
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import dash_bootstrap_components as dbc
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from core.chart_data_provider import ChartDataProvider
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# Setup logging with ASCII-only output
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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class CNNTradingDashboard:
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"""Lightweight Dash web interface for CNN trading predictions"""
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def __init__(self):
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# Initialize Dash app
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self.app = dash.Dash(
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__name__,
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external_stylesheets=[dbc.themes.BOOTSTRAP],
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title="CNN Trading Dashboard"
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)
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# Initialize core data provider
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self.data_provider = ChartDataProvider()
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# Setup web interface
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self._setup_layout()
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self._setup_callbacks()
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logger.info("CNN Trading Dashboard web interface initialized")
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def _setup_layout(self):
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"""Setup the web dashboard layout"""
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self.app.layout = dbc.Container([
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# Header
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dbc.Row([
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dbc.Col([
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html.H1("CNN Trading Dashboard",
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className="text-center text-primary mb-2"),
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html.P("Real-time CNN pivot predictions for ETH/USDT trading",
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className="text-center text-muted mb-4")
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])
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]),
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# Main chart
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dbc.Row([
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dbc.Col([
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dbc.Card([
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dbc.CardHeader([
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html.H4("Price Chart with CNN Predictions", className="mb-0")
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]),
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dbc.CardBody([
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dcc.Graph(
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id='main-chart',
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style={'height': '600px'},
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config={'displayModeBar': True}
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)
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])
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])
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], width=12)
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], className="mb-4"),
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# Status panels
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dbc.Row([
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# CNN Status
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dbc.Col([
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dbc.Card([
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dbc.CardHeader([
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html.H5("CNN Prediction Status", className="mb-0")
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]),
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dbc.CardBody([
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html.Div(id='cnn-status')
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])
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])
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], width=4),
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# Pivot Detection Status
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dbc.Col([
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dbc.Card([
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dbc.CardHeader([
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html.H5("Pivot Detection Status", className="mb-0")
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]),
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dbc.CardBody([
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html.Div(id='pivot-status')
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])
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])
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], width=4),
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# Training Data Status
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dbc.Col([
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dbc.Card([
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dbc.CardHeader([
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html.H5("Training Data Capture", className="mb-0")
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]),
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dbc.CardBody([
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html.Div(id='training-status')
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])
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])
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], width=4)
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], className="mb-4"),
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# System info
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dbc.Row([
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dbc.Col([
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dbc.Alert([
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html.H6("Legend:", className="mb-2"),
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html.Ul([
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html.Li("Hollow Red Circles: CNN HIGH pivot predictions"),
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html.Li("Hollow Green Circles: CNN LOW pivot predictions"),
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html.Li("Red Triangles: Actual HIGH pivots detected"),
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html.Li("Green Triangles: Actual LOW pivots detected"),
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html.Li("Circle/Triangle size indicates confidence/strength")
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], className="mb-0")
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], color="info", className="mb-3")
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])
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]),
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# Auto-refresh interval
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dcc.Interval(
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id='refresh-interval',
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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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], fluid=True)
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def _setup_callbacks(self):
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"""Setup Dash callbacks for web interface updates"""
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@self.app.callback(
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[Output('main-chart', 'figure'),
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Output('cnn-status', 'children'),
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Output('pivot-status', 'children'),
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Output('training-status', 'children')],
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[Input('refresh-interval', 'n_intervals')]
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)
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def update_dashboard(n_intervals):
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"""Main callback to update all dashboard components"""
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try:
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# Simulate price update
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self.data_provider.simulate_price_update()
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# Get updated predictions and pivots
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predictions, pivots = self.data_provider.update_predictions_and_pivots()
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# Create main chart
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fig = self.data_provider.create_price_chart()
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# Add predictions and pivots to chart
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fig = self.data_provider.add_cnn_predictions_to_chart(fig, predictions)
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fig = self.data_provider.add_actual_pivots_to_chart(fig, pivots)
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# Get status for info panels
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status = self.data_provider.get_current_status()
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# Create status displays
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cnn_status = self._create_cnn_status_display(status.get('predictions', {}))
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pivot_status = self._create_pivot_status_display(status.get('pivots', {}))
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training_status = self._create_training_status_display(status.get('training', {}))
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return fig, cnn_status, pivot_status, training_status
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except Exception as e:
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logger.error(f"Error updating dashboard: {e}")
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# Return empty/default values on error
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return {}, "Error loading CNN status", "Error loading pivot status", "Error loading training status"
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def _create_cnn_status_display(self, stats: dict) -> list:
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"""Create CNN status display components"""
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try:
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active_predictions = stats.get('active_predictions', 0)
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high_confidence = stats.get('high_confidence', 0)
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avg_confidence = stats.get('avg_confidence', 0)
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return [
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html.P(f"Active Predictions: {active_predictions}", className="mb-1"),
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html.P(f"High Confidence: {high_confidence}", className="mb-1"),
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html.P(f"Average Confidence: {avg_confidence:.1%}", className="mb-1"),
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dbc.Progress(
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value=avg_confidence * 100,
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color="success" if avg_confidence > 0.7 else "warning" if avg_confidence > 0.5 else "danger",
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className="mb-2"
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),
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html.Small(f"Last Update: {datetime.now().strftime('%H:%M:%S')}",
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className="text-muted")
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]
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except Exception as e:
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logger.error(f"Error creating CNN status display: {e}")
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return [html.P("Error loading CNN status")]
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def _create_pivot_status_display(self, stats: dict) -> list:
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"""Create pivot detection status display components"""
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try:
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total_pivots = stats.get('total_pivots', 0)
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high_pivots = stats.get('high_pivots', 0)
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low_pivots = stats.get('low_pivots', 0)
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confirmed = stats.get('confirmed_pivots', 0)
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return [
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html.P(f"Total Pivots: {total_pivots}", className="mb-1"),
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html.P(f"HIGH Pivots: {high_pivots}", className="mb-1"),
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html.P(f"LOW Pivots: {low_pivots}", className="mb-1"),
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html.P(f"Confirmed: {confirmed}", className="mb-1"),
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dbc.Progress(
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value=(confirmed / max(total_pivots, 1)) * 100,
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color="success",
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className="mb-2"
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),
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html.Small("Williams Market Structure", className="text-muted")
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]
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except Exception as e:
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logger.error(f"Error creating pivot status display: {e}")
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return [html.P("Error loading pivot status")]
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def _create_training_status_display(self, stats: dict) -> list:
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"""Create training data status display components"""
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try:
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captured_points = stats.get('captured_points', 0)
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price_accuracy = stats.get('avg_price_accuracy', 0)
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time_accuracy = stats.get('avg_time_accuracy', 0)
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return [
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html.P(f"Data Points: {captured_points}", className="mb-1"),
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html.P(f"Price Accuracy: {price_accuracy:.1%}", className="mb-1"),
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html.P(f"Time Accuracy: {time_accuracy:.1%}", className="mb-1"),
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dbc.Progress(
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value=price_accuracy * 100,
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color="success" if price_accuracy > 0.8 else "warning" if price_accuracy > 0.6 else "danger",
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className="mb-2"
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),
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html.Small("Auto-saved every 5 points", className="text-muted")
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]
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except Exception as e:
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logger.error(f"Error creating training status display: {e}")
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return [html.P("Error loading training status")]
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def run(self, host='127.0.0.1', port=8050, debug=False):
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"""Run the dashboard web server"""
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try:
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logger.info(f"Starting CNN Trading Dashboard at http://{host}:{port}")
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self.app.run_server(host=host, port=port, debug=debug)
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except Exception as e:
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logger.error(f"Error starting dashboard server: {e}")
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raise
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def main():
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"""Main entry point"""
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dashboard = CNNTradingDashboard()
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dashboard.run(debug=True)
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if __name__ == "__main__":
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main()
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