Infraestructura Avanzada de Trading

HARDWARE_SPECS = {
    'minimum': {
        'cpu': 'Intel i7 or AMD Ryzen 7',
        'ram': '16GB DDR4',
        'storage': '512GB SSD',
        'internet': '100 Mbps dedicated',
        'monitors': '2x 24" 1080p',
        'cost': '$1,500-2,000'
    },
    'recommended': {
        'cpu': 'Intel i9 or AMD Ryzen 9',
        'ram': '32GB DDR4',
        'storage': '1TB NVMe SSD + 2TB HDD',
        'internet': '1Gbps fiber with backup',
        'monitors': '3x 27" 1440p',
        'ups': 'APC 1500VA',
        'cost': '$3,000-4,000'
    },
    'professional': {
        'cpu': 'Intel Xeon or AMD Threadripper',
        'ram': '64GB+ DDR4',
        'storage': '2TB NVMe SSD RAID',
        'internet': 'Dedicated line + cellular backup',
        'monitors': '6x 32" 4K',
        'server': 'Cloud computing integration',
        'cost': '$8,000-15,000'
    }
}

class TradingTechStack:
    def __init__(self, level='intermediate'):
        self.level = level
        self.stack = self.build_stack()
    
    def build_stack(self):
        base_stack = {
            'operating_system': 'Windows 10/11 Pro',
            'data_providers': [
                'Polygon.io',  # Real-time data
                'IEX Cloud',   # Backup data
                'Quandl',      # Historical data
                'Alpha Vantage'  # Fundamentals
            ],
            'brokers': [
                'Interactive Brokers',  # Primary
                'TD Ameritrade',       # API backup
                'Alpaca',              # Commission-free
                'TradeStation'         # Advanced charts
            ],
            'programming': {
                'languages': ['Python', 'JavaScript', 'SQL'],
                'frameworks': ['pandas', 'numpy', 'scipy', 'matplotlib'],
                'databases': ['PostgreSQL', 'InfluxDB', 'Redis'],
                'message_queues': ['RabbitMQ', 'Apache Kafka']
            },
            'platforms': [
                'Jupyter Lab',      # Development
                'VS Code',          # IDE
                'DAS Trader Pro',   # Execution
                'TradingView Pro+', # Charts
                'Discord',          # Alerts
                'Slack'            # Team communication
            ]
        }
        
        if self.level == 'professional':
            base_stack.update({
                'cloud_services': ['AWS', 'Google Cloud', 'Azure'],
                'monitoring': ['Grafana', 'Prometheus', 'ELK Stack'],
                'deployment': ['Docker', 'Kubernetes', 'Terraform'],
                'additional_data': ['S&P Capital IQ', 'Bloomberg Terminal']
            })
        
        return base_stack

class RealTimeDataPipeline:
    def __init__(self):
        self.data_sources = {}
        self.processing_pipeline = []
        self.storage_systems = {}
        self.alert_systems = {}
    
    def setup_data_sources(self):
        """Configurar fuentes de datos"""
        # Polygon.io WebSocket
        self.data_sources['polygon'] = {
            'type': 'websocket',
            'endpoint': 'wss://socket.polygon.io/stocks',
            'auth': 'API_KEY',
            'data_types': ['trades', 'quotes', 'aggregates'],
            'latency': '1-5ms',
            'cost': '$99-249/month'
        }
        
        # IEX Cloud
        self.data_sources['iex'] = {
            'type': 'rest_api',
            'endpoint': 'https://cloud.iexapis.com/v1/',
            'auth': 'API_TOKEN',
            'data_types': ['quotes', 'news', 'fundamentals'],
            'latency': '100-500ms',
            'cost': '$9-499/month'
        }
        
        # Interactive Brokers TWS
        self.data_sources['ibkr'] = {
            'type': 'socket_api',
            'library': 'ib_insync',
            'data_types': ['market_data', 'account_info', 'orders'],
            'latency': '50-200ms',
            'cost': '$10/month + commissions'
        }
    
    def setup_processing_pipeline(self):
        """Pipeline de procesamiento"""
        stages = [
            {
                'stage': 'ingestion',
                'function': self.ingest_raw_data,
                'description': 'Recibir y normalizar data cruda'
            },
            {
                'stage': 'validation',
                'function': self.validate_data_quality,
                'description': 'Verificar calidad y consistencia'
            },
            {
                'stage': 'enrichment',
                'function': self.enrich_with_indicators,
                'description': 'Agregar indicadores técnicos'
            },
            {
                'stage': 'screening',
                'function': self.apply_screening_filters,
                'description': 'Aplicar filtros de estrategias'
            },
            {
                'stage': 'alerting',
                'function': self.generate_alerts,
                'description': 'Generar alertas y señales'
            },
            {
                'stage': 'storage',
                'function': self.store_processed_data,
                'description': 'Almacenar para análisis histórico'
            }
        ]
        
        self.processing_pipeline = stages
    
    def ingest_raw_data(self, data_stream):
        """Ingesta de datos en tiempo real"""
        normalized_data = {
            'timestamp': pd.Timestamp.now(),
            'symbol': data_stream['symbol'],
            'price': float(data_stream['price']),
            'volume': int(data_stream['volume']),
            'bid': float(data_stream.get('bid', 0)),
            'ask': float(data_stream.get('ask', 0)),
            'source': data_stream['source']
        }
        
        return normalized_data
    
    def enrich_with_indicators(self, market_data):
        """Enriquecer con indicadores en tiempo real"""
        # Mantener rolling window de datos
        symbol = market_data['symbol']
        
        if symbol not in self.rolling_data:
            self.rolling_data[symbol] = deque(maxlen=200)  # 200 periods
        
        self.rolling_data[symbol].append(market_data)
        
        # Calcular indicadores
        df = pd.DataFrame(list(self.rolling_data[symbol]))
        
        if len(df) >= 20:
            # VWAP
            df['vwap'] = (df['price'] * df['volume']).cumsum() / df['volume'].cumsum()
            
            # Moving averages
            df['sma_20'] = df['price'].rolling(20).mean()
            df['ema_9'] = df['price'].ewm(span=9).mean()
            
            # RSI
            df['rsi'] = calculate_rsi(df['price'], 14)
            
            # Volume analysis
            df['avg_volume'] = df['volume'].rolling(20).mean()
            df['volume_ratio'] = df['volume'] / df['avg_volume']
            
            # Update market data with indicators
            latest = df.iloc[-1]
            market_data.update({
                'vwap': latest['vwap'],
                'sma_20': latest['sma_20'],
                'ema_9': latest['ema_9'],
                'rsi': latest['rsi'],
                'volume_ratio': latest['volume_ratio']
            })
        
        return market_data

class RealTimeScreener:
    def __init__(self):
        self.strategies = {}
        self.active_alerts = {}
        self.screening_universe = set()
        
    def register_strategy(self, strategy_name, screening_function):
        """Registrar estrategia de screening"""
        self.strategies[strategy_name] = {
            'function': screening_function,
            'last_scan': None,
            'alerts_today': 0,
            'max_alerts_per_day': 50
        }
    
    def screen_market_data(self, market_data):
        """Aplicar screening a market data en tiempo real"""
        symbol = market_data['symbol']
        alerts = []
        
        # Solo screenear si el símbolo está en nuestro universo
        if symbol not in self.screening_universe:
            return alerts
        
        # Aplicar cada estrategia
        for strategy_name, strategy_info in self.strategies.items():
            try:
                # Check rate limiting
                if strategy_info['alerts_today'] >= strategy_info['max_alerts_per_day']:
                    continue
                
                # Apply screening function
                result = strategy_info['function'](market_data)
                
                if result and result['signal']:
                    alert = {
                        'timestamp': pd.Timestamp.now(),
                        'symbol': symbol,
                        'strategy': strategy_name,
                        'signal_type': result['signal_type'],
                        'confidence': result.get('confidence', 0.5),
                        'entry_price': result.get('entry_price'),
                        'stop_loss': result.get('stop_loss'),
                        'target': result.get('target'),
                        'message': result.get('message', f'{strategy_name} signal on {symbol}')
                    }
                    
                    alerts.append(alert)
                    strategy_info['alerts_today'] += 1
                    
            except Exception as e:
                print(f"Error in strategy {strategy_name}: {e}")
                continue
        
        return alerts

# Ejemplo de función de screening para Gap & Go
def gap_and_go_screener(market_data):
    """Screening para Gap & Go en tiempo real"""
    # Verificar si hay gap significativo
    if 'prev_close' not in market_data:
        return None
    
    gap_pct = (market_data['price'] - market_data['prev_close']) / market_data['prev_close']
    
    # Criterios básicos
    if gap_pct < 0.10:  # Menos de 10% gap
        return None
    
    if market_data.get('volume_ratio', 1) < 3:  # Menos de 3x volumen
        return None
    
    # Verificar si está manteniendo el gap
    if market_data['price'] < market_data.get('vwap', market_data['price']):
        return None
    
    # Si pasa todos los filtros, generar señal
    return {
        'signal': True,
        'signal_type': 'gap_and_go_continuation',
        'confidence': min(0.9, gap_pct / 0.20),  # Max confidence at 20% gap
        'entry_price': market_data['price'],
        'stop_loss': market_data['vwap'] * 0.97,
        'target': market_data['price'] * 1.15,
        'message': f"Gap & Go: {market_data['symbol']} gapped {gap_pct:.1%} with {market_data.get('volume_ratio', 0):.1f}x volume"
    }

import discord
from discord.ext import commands
import asyncio

class TradingAlertsBot:
    def __init__(self, token, channel_id):
        self.token = token
        self.channel_id = channel_id
        self.client = discord.Client()
        self.setup_events()
    
    def setup_events(self):
        @self.client.event
        async def on_ready():
            print(f'Alert bot logged in as {self.client.user}')
        
        @self.client.event
        async def on_message(message):
            if message.author == self.client.user:
                return
            
            # Responder a comandos básicos
            if message.content.startswith('!status'):
                await self.send_system_status(message.channel)
    
    async def send_trading_alert(self, alert_data):
        """Enviar alerta de trading a Discord"""
        channel = self.client.get_channel(self.channel_id)
        
        # Crear embed rico
        embed = discord.Embed(
            title=f"🚨 {alert_data['strategy'].upper()} ALERT",
            description=alert_data['message'],
            color=0x00ff00 if alert_data['signal_type'] == 'long' else 0xff0000,
            timestamp=alert_data['timestamp']
        )
        
        embed.add_field(name="Symbol", value=alert_data['symbol'], inline=True)
        embed.add_field(name="Entry", value=f"${alert_data['entry_price']:.2f}", inline=True)
        embed.add_field(name="Stop", value=f"${alert_data['stop_loss']:.2f}", inline=True)
        embed.add_field(name="Target", value=f"${alert_data['target']:.2f}", inline=True)
        embed.add_field(name="Confidence", value=f"{alert_data['confidence']:.1%}", inline=True)
        
        # Calcular R/R ratio
        risk = abs(alert_data['entry_price'] - alert_data['stop_loss'])
        reward = abs(alert_data['target'] - alert_data['entry_price'])
        rr_ratio = reward / risk if risk > 0 else 0
        
        embed.add_field(name="R/R Ratio", value=f"{rr_ratio:.1f}:1", inline=True)
        
        await channel.send(embed=embed)
    
    def start(self):
        """Iniciar bot de alertas"""
        self.client.run(self.token)

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

class EmailAlertSystem:
    def __init__(self, smtp_config):
        self.smtp_server = smtp_config['server']
        self.smtp_port = smtp_config['port']
        self.username = smtp_config['username']
        self.password = smtp_config['password']
        self.from_email = smtp_config['from_email']
    
    def send_critical_alert(self, to_emails, alert_data):
        """Enviar alerta crítica por email"""
        msg = MIMEMultipart()
        msg['From'] = self.from_email
        msg['To'] = ', '.join(to_emails)
        msg['Subject'] = f"CRITICAL TRADING ALERT: {alert_data['symbol']}"
        
        # Crear contenido HTML
        html_body = f"""
        <html>
        <body>
            <h2 style="color: red;">CRITICAL TRADING ALERT</h2>
            <table border="1" style="border-collapse: collapse;">
                <tr><td><b>Symbol:</b></td><td>{alert_data['symbol']}</td></tr>
                <tr><td><b>Strategy:</b></td><td>{alert_data['strategy']}</td></tr>
                <tr><td><b>Entry Price:</b></td><td>${alert_data['entry_price']:.2f}</td></tr>
                <tr><td><b>Stop Loss:</b></td><td>${alert_data['stop_loss']:.2f}</td></tr>
                <tr><td><b>Target:</b></td><td>${alert_data['target']:.2f}</td></tr>
                <tr><td><b>Confidence:</b></td><td>{alert_data['confidence']:.1%}</td></tr>
                <tr><td><b>Time:</b></td><td>{alert_data['timestamp']}</td></tr>
            </table>
            <p><b>Message:</b> {alert_data['message']}</p>
        </body>
        </html>
        """
        
        msg.attach(MIMEText(html_body, 'html'))
        
        # Enviar email
        try:
            server = smtplib.SMTP(self.smtp_server, self.smtp_port)
            server.starttls()
            server.login(self.username, self.password)
            server.send_message(msg)
            server.quit()
            return True
        except Exception as e:
            print(f"Error sending email: {e}")
            return False

class SystemHealthMonitor:
    def __init__(self):
        self.health_metrics = {}
        self.alert_thresholds = {
            'data_latency_ms': 1000,      # 1 second max
            'cpu_usage_pct': 80,          # 80% max
            'memory_usage_pct': 85,       # 85% max
            'disk_usage_pct': 90,         # 90% max
            'network_errors_per_min': 5,  # 5 errors max
            'api_response_time_ms': 500   # 500ms max
        }
    
    def collect_system_metrics(self):
        """Recopilar métricas del sistema"""
        import psutil
        
        self.health_metrics = {
            'timestamp': pd.Timestamp.now(),
            'cpu_usage_pct': psutil.cpu_percent(interval=1),
            'memory_usage_pct': psutil.virtual_memory().percent,
            'disk_usage_pct': psutil.disk_usage('/').percent,
            'network_io': psutil.net_io_counters(),
            'process_count': len(psutil.pids()),
            'uptime_hours': (pd.Timestamp.now() - self.start_time).total_seconds() / 3600
        }
        
        return self.health_metrics
    
    def check_system_health(self):
        """Verificar salud del sistema"""
        metrics = self.collect_system_metrics()
        alerts = []
        
        for metric, threshold in self.alert_thresholds.items():
            if metric in metrics and metrics[metric] > threshold:
                alerts.append({
                    'type': 'system_warning',
                    'metric': metric,
                    'current_value': metrics[metric],
                    'threshold': threshold,
                    'severity': 'high' if metrics[metric] > threshold * 1.2 else 'medium'
                })
        
        return alerts
    
    def test_data_connections(self):
        """Test conectividad con fuentes de datos"""
        connection_tests = {}
        
        # Test Polygon.io
        try:
            response = requests.get('https://api.polygon.io/v1/marketstatus/now', 
                                  params={'apikey': POLYGON_API_KEY}, timeout=5)
            connection_tests['polygon'] = {
                'status': 'healthy' if response.status_code == 200 else 'error',
                'response_time_ms': response.elapsed.total_seconds() * 1000,
                'last_test': pd.Timestamp.now()
            }
        except Exception as e:
            connection_tests['polygon'] = {
                'status': 'error',
                'error': str(e),
                'last_test': pd.Timestamp.now()
            }
        
        # Test other connections...
        return connection_tests

def calculate_infrastructure_costs():
    """Calcular costos mensuales de infraestructura"""
    
    monthly_costs = {
        'data_feeds': {
            'polygon_io_developer': 99,
            'iex_cloud_scale': 99,
            'tradingview_pro_plus': 60,
            'total': 258
        },
        'brokers': {
            'interactive_brokers': 10,
            'das_trader_pro': 150,
            'tradingview_alerts': 15,
            'total': 175
        },
        'cloud_services': {
            'aws_ec2_t3_large': 60,
            'aws_rds_postgres': 45,
            'aws_s3_storage': 25,
            'digital_ocean_backup': 20,
            'total': 150
        },
        'software': {
            'office_365': 15,
            'discord_nitro': 10,
            'github_pro': 4,
            'total': 29
        },
        'communications': {
            'dedicated_internet': 150,
            'backup_cellular': 50,
            'voip_service': 25,
            'total': 225
        }
    }
    
    total_monthly = sum(category['total'] for category in monthly_costs.values())
    annual_cost = total_monthly * 12
    
    # One-time setup costs
    setup_costs = {
        'hardware': 4000,
        'software_licenses': 1500,
        'initial_development': 5000,
        'total': 10500
    }
    
    return {
        'monthly_operational': total_monthly,
        'annual_operational': annual_cost,
        'setup_costs': setup_costs['total'],
        'total_first_year': annual_cost + setup_costs['total'],
        'breakdown': monthly_costs
    }

def calculate_breakeven_analysis(infrastructure_costs, trading_capital):
    """Calcular análisis de break-even"""
    
    annual_cost = infrastructure_costs['annual_operational']
    
    # Retornos necesarios para break-even
    breakeven_scenarios = {
        'conservative': {
            'required_annual_return_pct': annual_cost / trading_capital,
            'required_monthly_return_pct': (annual_cost / trading_capital) / 12,
            'trades_per_month': 20,
            'avg_return_per_trade_required': (annual_cost / 12) / (trading_capital * 0.02) / 20
        },
        'realistic': {
            'target_annual_return_pct': 0.25,  # 25%
            'required_capital_for_breakeven': annual_cost / 0.25,
            'profit_after_costs': trading_capital * 0.25 - annual_cost
        }
    }
    
    return breakeven_scenarios

class DisasterRecoveryPlan:
    def __init__(self):
        self.backup_systems = {}
        self.recovery_procedures = {}
        
    def setup_backup_systems(self):
        """Configurar sistemas de backup"""
        self.backup_systems = {
            'data_backup': {
                'primary': 'AWS S3 with versioning',
                'secondary': 'Google Cloud Storage',
                'frequency': 'Real-time replication',
                'retention': '7 years'
            },
            'code_backup': {
                'primary': 'GitHub private repos',
                'secondary': 'GitLab backup',
                'frequency': 'Every commit',
                'retention': 'Indefinite'
            },
            'system_backup': {
                'primary': 'Full system images weekly',
                'secondary': 'Incremental daily',
                'frequency': 'Daily incremental, weekly full',
                'retention': '90 days'
            }
        }
    
    def create_recovery_procedures(self):
        """Procedimientos de recuperación"""
        self.recovery_procedures = {
            'internet_outage': {
                'immediate_actions': [
                    'Switch to cellular backup',
                    'Notify broker of connectivity issues',
                    'Close all open positions if critical'
                ],
                'recovery_time': '5 minutes',
                'contact_list': ['ISP support', 'Cellular provider']
            },
            'hardware_failure': {
                'immediate_actions': [
                    'Switch to backup computer',
                    'Access cloud-based trading platform',
                    'Download latest data snapshot'
                ],
                'recovery_time': '15 minutes',
                'backup_hardware': 'Secondary trading computer ready'
            },
            'broker_outage': {
                'immediate_actions': [
                    'Switch to backup broker account',
                    'Hedge positions if possible',
                    'Monitor via alternative platforms'
                ],
                'recovery_time': '10 minutes',
                'backup_brokers': ['TD Ameritrade', 'Alpaca']
            }
        }