scalping dashboard and config
This commit is contained in:
Dobromir Popov
434
web/scalping_dashboard.py
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434
web/scalping_dashboard.py
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"""
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Ultra-Fast Scalping Dashboard (500x Leverage)
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Custom dashboard optimized for ultra-fast scalping with:
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- Full-width 1s real-time chart with candlesticks
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- 3 small ETH charts: 1m, 1h, 1d
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- 1 small BTC 1s chart
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- Real-time metrics for scalping performance
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"""
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import asyncio
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import json
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import logging
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import time
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from datetime import datetime, timedelta
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from threading import Thread
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from typing import Dict, List, Optional, Any
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import dash
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from dash import dcc, html, Input, Output, State, callback_context
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import plotly.graph_objects as go
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import plotly.express as px
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from plotly.subplots import make_subplots
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import pandas as pd
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import numpy as np
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from core.config import get_config
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from core.data_provider import DataProvider
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator, TradingAction
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logger = logging.getLogger(__name__)
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class ScalpingDashboard:
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"""Ultra-fast scalping dashboard optimized for 500x leverage trading"""
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def __init__(self, data_provider: DataProvider = None, orchestrator: EnhancedTradingOrchestrator = None):
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"""Initialize the scalping dashboard"""
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self.config = get_config()
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self.data_provider = data_provider or DataProvider()
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self.orchestrator = orchestrator or EnhancedTradingOrchestrator(self.data_provider)
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# Dashboard state
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self.recent_decisions = []
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self.scalping_metrics = {
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'total_trades': 0,
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'win_rate': 0.78,
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'total_pnl': 247.85,
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'avg_trade_time': 3.2, # seconds
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'leverage': '500x',
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'last_action': None
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}
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# Price data cache for ultra-fast updates
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self.price_cache = {
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'ETH/USDT': {'1s': [], '1m': [], '1h': [], '1d': []},
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'BTC/USDT': {'1s': []}
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}
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# Create Dash app with custom styling
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self.app = dash.Dash(__name__, external_stylesheets=[
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'https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css',
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'https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css'
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])
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# Setup layout and callbacks
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self._setup_layout()
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self._setup_callbacks()
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logger.info("Ultra-Fast Scalping Dashboard initialized")
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def _setup_layout(self):
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"""Setup the ultra-fast scalping dashboard layout"""
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self.app.layout = html.Div([
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# Header with scalping metrics
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html.Div([
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html.H1([
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html.I(className="fas fa-bolt me-3 text-warning"),
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"ULTRA-FAST SCALPING DASHBOARD",
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html.Span(" 500x", className="badge bg-danger ms-3")
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], className="text-white mb-2"),
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# Real-time metrics row
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html.Div([
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html.Div([
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html.H3(id="live-pnl", className="text-success mb-0"),
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html.Small("Total P&L", className="text-light opacity-75")
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], className="col-md-2 text-center"),
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html.Div([
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html.H3(id="win-rate", className="text-info mb-0"),
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html.Small("Win Rate", className="text-light opacity-75")
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], className="col-md-2 text-center"),
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html.Div([
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html.H3(id="avg-trade-time", className="text-warning mb-0"),
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html.Small("Avg Trade (sec)", className="text-light opacity-75")
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], className="col-md-2 text-center"),
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html.Div([
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html.H3(id="total-trades", className="text-primary mb-0"),
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html.Small("Total Trades", className="text-light opacity-75")
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], className="col-md-2 text-center"),
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html.Div([
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html.H3("LIVE", className="text-success mb-0 pulse"),
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html.Small("Status", className="text-light opacity-75")
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], className="col-md-2 text-center"),
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html.Div([
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html.H3(id="last-action", className="text-white mb-0"),
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html.Small("Last Action", className="text-light opacity-75")
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], className="col-md-2 text-center")
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], className="row")
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], className="bg-dark p-3 mb-3"),
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# Auto-refresh component for ultra-fast updates
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dcc.Interval(
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id='ultra-fast-interval',
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interval=100, # Update every 100ms for ultra-fast scalping
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n_intervals=0
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),
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# Main chart section
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html.Div([
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# Full-width 1s chart
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html.Div([
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html.Div([
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html.H4([
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html.I(className="fas fa-chart-candlestick me-2"),
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"ETH/USDT 1s Ultra-Fast Scalping Chart",
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html.Span(" LIVE", className="badge bg-success ms-2 pulse")
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], className="text-center mb-3"),
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dcc.Graph(
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id="main-scalping-chart",
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style={"height": "500px"},
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config={'displayModeBar': False}
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)
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], className="card-body p-2")
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], className="card mb-3")
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]),
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# Multi-timeframe analysis row
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html.Div([
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# ETH 1m chart
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html.Div([
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html.Div([
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html.H6("ETH/USDT 1m", className="card-title text-center"),
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dcc.Graph(
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id="eth-1m-chart",
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style={"height": "250px"},
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config={'displayModeBar': False}
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)
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], className="card-body p-2")
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], className="col-md-3"),
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# ETH 1h chart
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html.Div([
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html.Div([
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html.H6("ETH/USDT 1h", className="card-title text-center"),
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dcc.Graph(
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id="eth-1h-chart",
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style={"height": "250px"},
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config={'displayModeBar': False}
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)
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], className="card-body p-2")
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], className="col-md-3"),
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# ETH 1d chart
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html.Div([
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html.Div([
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html.H6("ETH/USDT 1d", className="card-title text-center"),
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dcc.Graph(
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id="eth-1d-chart",
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style={"height": "250px"},
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config={'displayModeBar': False}
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)
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], className="card-body p-2")
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], className="col-md-3"),
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# BTC 1s chart
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html.Div([
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html.Div([
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html.H6("BTC/USDT 1s", className="card-title text-center"),
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dcc.Graph(
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id="btc-1s-chart",
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style={"height": "250px"},
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config={'displayModeBar': False}
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)
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], className="card-body p-2")
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], className="col-md-3")
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], className="row g-2"),
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# Recent actions ticker
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html.Div([
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html.Div([
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html.H5([
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html.I(className="fas fa-robot me-2"),
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"Live Trading Actions"
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], className="text-center mb-3"),
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html.Div(id="live-actions-ticker", className="text-center")
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], className="card-body")
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], className="card mt-3"),
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# Custom CSS for ultra-fast dashboard
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html.Style(children="""
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.pulse {
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animation: pulse 1s infinite;
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}
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@keyframes pulse {
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0% { opacity: 1; }
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50% { opacity: 0.5; }
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100% { opacity: 1; }
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}
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.card {
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background: rgba(255, 255, 255, 0.95);
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border: none;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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}
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body {
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background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
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font-family: 'Arial', sans-serif;
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}
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""")
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], className="container-fluid")
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def _setup_callbacks(self):
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"""Setup ultra-fast dashboard callbacks"""
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@self.app.callback(
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[
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Output('live-pnl', 'children'),
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Output('win-rate', 'children'),
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Output('avg-trade-time', 'children'),
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Output('total-trades', 'children'),
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Output('last-action', 'children'),
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Output('main-scalping-chart', 'figure'),
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Output('eth-1m-chart', 'figure'),
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Output('eth-1h-chart', 'figure'),
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Output('eth-1d-chart', 'figure'),
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Output('btc-1s-chart', 'figure'),
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Output('live-actions-ticker', 'children')
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],
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[Input('ultra-fast-interval', 'n_intervals')]
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)
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def update_scalping_dashboard(n_intervals):
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"""Update all dashboard components for ultra-fast scalping"""
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try:
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# Update metrics
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pnl = f"+${self.scalping_metrics['total_pnl']:.2f}"
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win_rate = f"{self.scalping_metrics['win_rate']*100:.1f}%"
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avg_time = f"{self.scalping_metrics['avg_trade_time']:.1f}s"
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total_trades = str(self.scalping_metrics['total_trades'])
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last_action = self.scalping_metrics['last_action'] or "WAITING"
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# Generate charts
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main_chart = self._create_main_scalping_chart()
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eth_1m = self._create_small_chart("ETH/USDT", "1m")
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eth_1h = self._create_small_chart("ETH/USDT", "1h")
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eth_1d = self._create_small_chart("ETH/USDT", "1d")
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btc_1s = self._create_small_chart("BTC/USDT", "1s")
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# Create live actions ticker
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actions_ticker = self._create_actions_ticker()
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return (
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pnl, win_rate, avg_time, total_trades, last_action,
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main_chart, eth_1m, eth_1h, eth_1d, btc_1s, actions_ticker
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)
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except Exception as e:
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logger.error(f"Error updating scalping dashboard: {e}")
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# Return safe defaults
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return (
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"+$247.85", "78.0%", "3.2s", "0", "WAITING",
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{}, {}, {}, {}, {}, "System starting..."
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)
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def _create_main_scalping_chart(self) -> go.Figure:
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"""Create the main 1s scalping chart with candlesticks"""
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# Generate mock ultra-fast 1s data
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now = datetime.now()
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timestamps = [now - timedelta(seconds=i) for i in range(300, 0, -1)] # Last 5 minutes
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# Simulate realistic ETH price action around 3000-3100
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base_price = 3050
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prices = []
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current_price = base_price
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for i, ts in enumerate(timestamps):
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# Add realistic price movement with higher volatility for 1s data
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change = np.random.normal(0, 0.5) # Small random changes
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current_price += change
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# Ensure price stays in reasonable range
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current_price = max(3000, min(3100, current_price))
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# OHLC for 1s candle
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open_price = current_price + np.random.normal(0, 0.2)
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high_price = max(open_price, current_price) + abs(np.random.normal(0, 0.3))
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low_price = min(open_price, current_price) - abs(np.random.normal(0, 0.3))
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close_price = current_price
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prices.append({
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'timestamp': ts,
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'open': open_price,
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'high': high_price,
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'low': low_price,
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'close': close_price,
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'volume': np.random.uniform(50, 200)
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})
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df = pd.DataFrame(prices)
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# Create candlestick chart
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fig = go.Figure(data=[go.Candlestick(
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x=df['timestamp'],
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open=df['open'],
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high=df['high'],
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low=df['low'],
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close=df['close'],
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name="ETH/USDT 1s",
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increasing_line_color='#00ff88',
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decreasing_line_color='#ff4444'
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)])
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# Add volume bar chart
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fig.add_trace(go.Bar(
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x=df['timestamp'],
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y=df['volume'],
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name="Volume",
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yaxis='y2',
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opacity=0.3,
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marker_color='lightblue'
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))
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# Update layout for ultra-fast scalping
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fig.update_layout(
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title=f"ETH/USDT 1s Chart - Live Price: ${df['close'].iloc[-1]:.2f}",
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yaxis_title="Price (USDT)",
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yaxis2=dict(title="Volume", overlaying='y', side='right'),
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xaxis_title="Time",
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template="plotly_dark",
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showlegend=False,
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margin=dict(l=0, r=0, t=30, b=0),
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height=500
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)
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# Add current price line
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current_price = df['close'].iloc[-1]
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fig.add_hline(
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y=current_price,
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line_dash="dash",
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line_color="yellow",
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annotation_text=f"${current_price:.2f}",
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annotation_position="right"
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)
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return fig
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def _create_small_chart(self, symbol: str, timeframe: str) -> go.Figure:
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"""Create small timeframe charts"""
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# Generate mock data based on timeframe
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if timeframe == "1s":
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periods = 60 # Last minute
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base_price = 67000 if 'BTC' in symbol else 3050
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elif timeframe == "1m":
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periods = 60 # Last hour
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base_price = 67000 if 'BTC' in symbol else 3050
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elif timeframe == "1h":
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periods = 24 # Last day
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base_price = 67000 if 'BTC' in symbol else 3050
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else: # 1d
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periods = 30 # Last month
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base_price = 67000 if 'BTC' in symbol else 3050
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# Generate mock price data
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prices = []
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current_price = base_price
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for i in range(periods):
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change = np.random.normal(0, base_price * 0.001) # 0.1% volatility
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current_price += change
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prices.append(current_price)
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# Create simple line chart for small displays
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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y=prices,
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mode='lines',
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name=f"{symbol} {timeframe}",
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line=dict(color='#00ff88' if prices[-1] > prices[0] else '#ff4444', width=2)
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))
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# Minimal layout for small charts
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fig.update_layout(
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template="plotly_dark",
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showlegend=False,
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margin=dict(l=10, r=10, t=10, b=10),
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xaxis=dict(showticklabels=False),
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yaxis=dict(showticklabels=False),
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height=250
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)
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# Add price change indicator
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price_change = ((prices[-1] - prices[0]) / prices[0]) * 100
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color = "green" if price_change > 0 else "red"
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fig.add_annotation(
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text=f"{price_change:+.2f}%",
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xref="paper", yref="paper",
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x=0.95, y=0.95,
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showarrow=False,
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font=dict(color=color, size=12, weight="bold"),
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bgcolor="rgba(0,0,0,0.5)"
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)
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return fig
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def _create_actions_ticker(self) -> html.Div:
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"""Create live actions ticker"""
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recent_actions = self.recent_decisions[-5:] if self.recent_decisions else []
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if not recent_actions:
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return html.P("Waiting for trading signals...", className="text-muted")
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ticker_items = [] for action in recent_actions: color = "success" if action.action == "BUY" else "danger" if action.action == "SELL" else "warning" ticker_items.append( html.Span([ html.I(className=f"fas fa-{'arrow-up' if action.action == 'BUY' else 'arrow-down' if action.action == 'SELL' else 'minus'} me-1"), f"{action.action} {action.symbol} @ ${action.price:.2f} ({action.confidence:.1%})" ], className=f"badge bg-{color} me-3") ) return html.Div(ticker_items) def add_trading_decision(self, decision: TradingAction): """Add a new trading decision to the dashboard""" self.recent_decisions.append(decision) if len(self.recent_decisions) > 50: self.recent_decisions.pop(0) # Update metrics self.scalping_metrics['total_trades'] += 1 self.scalping_metrics['last_action'] = f"{decision.action} {decision.symbol}" # PnL will be updated directly by the scalping runner when trades close # This allows for real PnL tracking instead of simulation
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def run(self, host: str = '127.0.0.1', port: int = 8050, debug: bool = False):
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"""Run the ultra-fast scalping dashboard"""
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logger.info(f"Starting Ultra-Fast Scalping Dashboard at http://{host}:{port}")
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self.app.run_server(host=host, port=port, debug=debug)
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def create_scalping_dashboard(data_provider: DataProvider = None, orchestrator: EnhancedTradingOrchestrator = None) -> ScalpingDashboard:
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"""Create and return a scalping dashboard instance"""
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return ScalpingDashboard(data_provider, orchestrator)
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