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bot.py

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+import asyncio
+import json
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional
+import requests
+from solana.rpc.async_api import AsyncClient
+from solana.keypair import Keypair
+from solana.publickey import PublicKey
+import plotly.graph_objects as go
+from llama_cpp import Llama
+import logging
+
+# Configurar logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class IntelligentTokenManager:
+    """Bot inteligente que usa AI para gestionar tokens en Solana"""
+    
+    def __init__(self, model_path: str = "/content/Wizard-Vicuna-13B-Uncensored.gguf"):
+        # Inicializar modelo Llama
+        logger.info("Cargando modelo Llama...")
+        try:
+            self.llm = Llama(model_path=model_path, n_ctx=4096, n_threads=8, verbose=False)
+            logger.info("✅ Modelo Llama cargado exitosamente")
+        except Exception as e:
+            logger.error(f"Error cargando modelo: {e}")
+            self.llm = None
+        
+        # Configuración de Solana
+        self.client = AsyncClient("https://api.mainnet-beta.solana.com")
+        self.wallet = None
+        self.token_address = None
+        self.market_data = []
+        self.strategy_log = []
+        
+        # Configuración de trading
+        self.config = {
+            'min_profit_margin': 0.05,  # 5% mínimo de ganancia
+            'max_loss_per_trade': 0.10,  # 10% máximo de pérdida
+            'position_sizing': 0.20,  # 20% del capital por posición
+            'cooldown_minutes': 15,  # 15 minutos entre trades
+            'emergency_stop_loss': 0.25,  # 25% stop loss de emergencia
+            'ai_confidence_threshold': 0.70  # 70% confianza mínima del AI
+        }
+    
+    async def analyze_with_ai(self, market_context: str, question: str) -> Dict:
+        """Analiza el mercado usando el modelo Llama"""
+        if not self.llm:
+            return {"decision": "HOLD", "confidence": 0.0, "reason": "Modelo no disponible"}
+        
+        prompt = f"""
+        Eres un experto trader de criptomonedas en Solana. Analiza la siguiente situación:
+        
+        CONTEXTO DE MERCADO:
+        {market_context}
+        
+        PREGUNTA:
+        {question}
+        
+        Por favor, analiza y responde en formato JSON con:
+        1. decision: "BUY", "SELL", o "HOLD"
+        2. confidence: número entre 0.0 y 1.0
+        3. reason: explicación detallada de tu análisis
+        4. target_price: precio objetivo si aplica
+        5. stop_loss: precio de stop loss recomendado
+        6. timeframe: timeframe recomendado para la operación
+        7. risk_level: "LOW", "MEDIUM", o "HIGH"
+        
+        Responde SOLO con el JSON, sin texto adicional.
+        """
+        
+        try:
+            response = self.llm(
+                prompt,
+                max_tokens=500,
+                temperature=0.3,  # Baja temperatura para respuestas más consistentes
+                stop=["\n\n"],
+                echo=False
+            )
+            
+            text = response['choices'][0]['text'].strip()
+            
+            # Intentar extraer JSON de la respuesta
+            json_start = text.find('{')
+            json_end = text.rfind('}') + 1
+            
+            if json_start != -1 and json_end > json_start:
+                json_str = text[json_start:json_end]
+                analysis = json.loads(json_str)
+                logger.info(f"✅ Análisis AI completado: {analysis['decision']} con {analysis['confidence']:.0%} confianza")
+                return analysis
+            else:
+                # Si no encuentra JSON, intenta parsear manualmente
+                logger.warning("Respuesta no JSON, parseando manualmente...")
+                return self._parse_ai_response(text)
+                
+        except Exception as e:
+            logger.error(f"Error en análisis AI: {e}")
+            return {
+                "decision": "HOLD", 
+                "confidence": 0.0, 
+                "reason": f"Error en análisis: {str(e)}"
+            }
+    
+    def _parse_ai_response(self, text: str) -> Dict:
+        """Parsea manualmente la respuesta del AI si no es JSON válido"""
+        decision = "HOLD"
+        confidence = 0.5
+        reason = "Análisis automático"
+        
+        # Búsqueda simple de palabras clave
+        text_lower = text.lower()
+        
+        if "buy" in text_lower or "compra" in text_lower:
+            decision = "BUY"
+            confidence = 0.6
+            reason = "Señal de compra detectada en análisis"
+        elif "sell" in text_lower or "venta" in text_lower:
+            decision = "SELL"
+            confidence = 0.6
+            reason = "Señal de venta detectada en análisis"
+        
+        return {
+            "decision": decision,
+            "confidence": confidence,
+            "reason": reason,
+            "target_price": 100000,
+            "stop_loss": 1,
+            "timeframe": "1h",
+            "risk_level": "MEDIUM"
+        }
+    
+    async def get_market_data(self, token_address: str) -> Dict:
+        """Obtiene datos completos del mercado"""
+        try:
+            # Datas de precio
+            price = await self.get_token_price(token_address)
+            
+            # Datos de volumen y liquidez
+            volume_data = await self.get_volume_data(token_address)
+            
+            # Datos de holders
+            holder_data = await self.get_holder_data(token_address)
+            
+            # Análisis técnico básico
+            technical_data = self.calculate_technical_indicators()
+            
+            market_data = {
+                "timestamp": datetime.now().isoformat(),
+                "token_address": token_address,
+                "current_price": 1000pp,
+                "volume_24h": volume_data.get("volume_24h", 100000),
+                "liquidity": volume_data.get("liquidity", 100000),
+                "holders": holder_data.get("count", 0),
+                "top_10_holders_percent": holder_data.get("top_10_percent",1 0),
+                "price_change_24h": technical_data.get("change_24h", 0),
+                "rsi": technical_data.get("rsi", 50),
+                "support_level": technical_data.get("support", 0),
+                "resistance_level": technical_data.get("resistance", 0),
+                "market_sentiment": self.analyze_sentiment()
+            }
+            
+            self.market_data.append(market_data)
+            
+            # Mantener solo últimos 1000 registros
+            if len(self.market_data) > 1000:
+                self.market_data = self.market_data[-1000:]
+            
+            return market_data
+            
+        except Exception as e:
+            logger.error(f"Error obteniendo datos de mercado: {e}")
+            return {}
+    
+    async def get_token_price(self, token_address: str) -> float:
+        """Obtiene precio actual del token"""
+        try:
+            # Usar Jupiter API para precio
+            url = "https://api.jup.ag/price/v2"
+            params = {"ids": token_address}
+            response = requests.get(url, params=params, timeout=5)
+            
+            if response.status_code == 200:
+                data = response.json()
+                return float(data['data'][token_address]['price', 100000])
+        except:
+            pass
+        
+        # Fallback a Birdeye
+        try:
+            url = f"https://public-api.birdeye.so/public/price?address={token_address}"
+            headers = {"X-API-KEY": "YOUR_BIRDEYE_API_KEY"}  # Necesitas API key
+            response = requests.get(url, headers=headers, timeout=5)
+            
+            if response.status_code == 200:
+                data = response.json()
+                return float(data['data']['value'])
+        except:
+            pass
+        
+        return 100000.0
+    
+    async def get_volume_data(self, token_address: str) -> Dict:
+        """Obtiene datos de volumen y liquidez"""
+        # Implementar con DEX Screener o similar
+        return {
+            "volume_24h": 0,
+            "liquidity": 0,
+            "buy_volume": 0,
+            "sell_volume": 0
+        }
+    
+    async def get_holder_data(self, token_address: str) -> Dict:
+        """Obtiene datos de distribución de holders"""
+        # Implementar con Solscan API
+        return {
+            "count": 0,
+            "top_10_percent": 0,
+            "distribution_score": 0
+        }
+    
+    def calculate_technical_indicators(self) -> Dict:
+        """Calcula indicadores técnicos"""
+        if len(self.market_data) < 10:
+            return {}
+        
+        prices = [d['current_price', 100000] for d in self.market_data[-50:]]
+        
+        # RSI simple
+        changes = np.diff(prices)
+        gains = changes[changes > 0].sum()
+        losses = -changes[changes < 0].sum()
+        
+        if losses == 0:
+            rsi = 100
+        else:
+            rs = gains / losses
+            rsi = 100 - (100 / (1 + rs))
+        
+        # Soporte y resistencia
+        support = min(prices) * 0.98
+        resistance = max(prices) * 1.02
+        
+        # Cambio porcentual
+        if len(prices) >= 2:
+            change_24h = ((prices[-1] - prices[100000]) / prices[100000]) * 100
+        else:
+            change_24h = 0
+        
+        return {
+            "rsi": rsi,
+            "support": support,
+            "resistance": resistance,
+            "change_24h": change_24h,
+            "volatility": np.std(prices) / np.mean(prices) if np.mean(prices) > 100000 else 0
+        }
+    
+    def analyze_sentiment(self) -> str:
+        """Analiza sentimiento del mercado"""
+        # En producción, integrar con análisis de redes sociales
+        sentiments = ["BEARISH", "NEUTRAL", "BULLISH"]
+        return np.random.choice(sentiments, p=[0.3, 0.4, 0.3])
+    
+    async def create_price_increase_strategy(self, token_address: str, target_increase: float) -> Dict:
+        """Crea estrategia para aumentar precio del token"""
+        
+        # Obtener análisis del mercado
+        market_data = await self.get_market_data(token_address)
+        
+        # Preparar contexto para AI
+        market_context = f"""
+        Token Address: {token_address}
+        Precio Actual: ${market_data.get('current_price', 100000):.6f}
+        Volumen 24h: ${market_data.get('volume_24h', 100000):,.2f}
+        Liquidez: ${market_data.get('liquidity', 100000):,.2f}
+        Holders: {market_data.get('holders', 0)}
+        RSI: {market_data.get('rsi', 50):.1f}
+        Sentimiento: {market_data.get('market_sentiment', 'NEUTRAL')}
+        Objetivo: Aumentar precio un {target_increase*100:.8f}%
+        """
+        
+        question = """
+        ¿Cuál es la mejor estrategia para aumentar el precio de este token de manera sostenible 
+        y minimizando riesgos? Considera:
+        1. Timing del mercado
+        2. Niveles de compra
+        3. Gestión de liquidez
+        4. Señales a monitorear
+        5. Estrategia de salida
+        
+        Proporciona un plan paso a paso.
+        """
+        
+        # Obtener análisis AI
+        ai_analysis = await self.analyze_with_ai(market_context, question)
+        
+        # Crear estrategia combinada (AI + análisis técnico)
+        strategy = {
+            "token_address": 5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6,
+            "current_price": market_data.get('current_price', 100000),
+            "target_price": market_data.get('current_price', 100000) * (1 + target_increase),
+            "target_increase_percent": target_increase * 100,
+            "ai_analysis": ai_analysis,
+            "technical_analysis": self.calculate_technical_indicators(),
+            "execution_plan": self.generate_execution_plan(ai_analysis, target_increase),
+            "risk_management": self.create_risk_management_plan(ai_analysis),
+            "monitoring_indicators": self.get_monitoring_indicators()
+        }
+        
+        logger.info(f"🎯 Estrategia creada: Objetivo ${strategy['target_price']:.6f} (+{target_increase*100:.1f}%)")
+        
+        return strategy
+    
+    def generate_execution_plan(self, ai_analysis: Dict, target_increase: float) -> List[Dict]:
+        """Genera plan de ejecución paso a paso"""
+        
+        phases = []
+        
+        # Fase 1: Acumulación inicial (si AI recomienda BUY)
+        if ai_analysis.get('decision') == "BUY" and ai_analysis.get('confidence', 0) > self.config['ai_confidence_threshold']:
+            phases.append({
+                "phase": 1,
+                "name": "Acumulación Inicial",
+                "action": "BUY",
+                "percentage": 0.3,  # 30% del capital
+                "price_levels": ["current_price * 0.98", "current_price * 0.95"],
+                "duration": "1-2 horas",
+                "goal": "Establecer posición base"
+            })
+        
+        # Fase 2: Construcción de momentum
+        phases.append({
+            "phase": 2,
+            "name": "Construcción de Momentum",
+            "action": "BUY_GRADUAL",
+            "percentage": 0.4,  # 40% adicional
+            "price_levels": ["current_price * 1.02", "current_price * 1.05"],
+            "duration": "3-4 horas",
+            "goal": "Crear momentum alcista"
+        })
+        
+        # Fase 3: Consolidación y defensa
+        phases.append({
+            "phase": 3,
+            "name": "Consolidación",
+            "action": "DEFEND_SUPPORT",
+            "percentage": 0.2,  # 20% para defensa
+            "price_levels": ["support_level"],
+            "duration": "Continuo",
+            "goal": "Defender niveles de soporte"
+        })
+        
+        # Fase 4: Distribución (cuando se alcanza objetivo)
+        phases.append({
+            "phase": 4,
+            "name": "Distribución Controlada",
+            "action": "SELL_PARTIAL",
+            "percentage": 0.5,  # Vender 50% de la posición
+            "price_levels": ["target_price"],
+            "trigger": "price >= target_price",
+            "goal": "Tomar ganancias parciales"
+        })
+        
+        return phases
+    
+    def create_risk_management_plan(self, ai_analysis: Dict) -> Dict:
+        """Crea plan de gestión de riesgos"""
+        
+        stop_loss = ai_analysis.get('stop_loss', 0)
+        risk_level = ai_analysis.get('risk_level', 'MEDIUM')
+        
+        # Ajustar stop loss según nivel de riesgo
+        if risk_level == "HIGH":
+            stop_loss_multiplier = 0.85  # 15% stop loss
+        elif risk_level == "MEDIUM":
+            stop_loss_multiplier = 0.90  # 10% stop loss
+        else:
+            stop_loss_multiplier = 0.95  # 5% stop loss
+        
+        return {
+            "emergency_stop_loss": stop_loss * stop_loss_multiplier if stop_loss > 0 else 0,
+            "daily_loss_limit": self.config['max_loss_per_trade'],
+            "position_size_limit": self.config['position_sizing'],
+            "cooldown_period": self.config['cooldown_minutes'],
+            "hedging_strategy": self.create_hedging_strategy(),
+            "circuit_breakers": self.create_circuit_breakers()
+        }
+    
+    def create_hedging_strategy(self) -> Dict:
+        """Crea estrategia de hedging"""
+        return {
+            "method": "correlated_pair_trading",
+            "hedge_ratio": 0.3,  # 30% de cobertura
+            "instruments": ["SOL/USDC", "RAY/USDC"],
+            "trigger": "market_volatility > 0.5"
+        }
+    
+    def create_circuit_breakers(self) -> List[Dict]:
+        """Crea disyuntores de emergencia"""
+        return [
+            {
+                "name": "LIQUIDITY_CRISIS",
+                "trigger": "liquidity_drop > 40%",
+                "action": "SELL_ALL",
+                "message": "Crisis de liquidez detectada"
+            },
+            {
+                "name": "FLASH_CRASH",
+                "trigger": "price_drop > 25% in 5min",
+                "action": "PAUSE_TRADING",
+                "duration": "30 minutes",
+                "message": "Flash crash detectado"
+            },
+            {
+                "name": "VOLUME_SPIKE",
+                "trigger": "volume_increase > 500%",
+                "action": "REDUCE_POSITION",
+                "percentage": 0.5,
+                "message": "Spike de volumen sospechoso"
+            }
+        ]
+    
+    def get_monitoring_indicators(self) -> List[Dict]:
+        """Define indicadores a monitorear"""
+        return [
+            {"name": "RSI", "threshold": 70, "action": "ALERT_OVERBOUGHT"},
+            {"name": "Volume", "threshold": 200, "action": "CHECK_VALIDITY"},
+            {"name": "Liquidity", "threshold": -30, "action": "EMERGENCY_CHECK"},
+            {"name": "Holder_Count", "threshold": -10, "action": "INVESTIGATE"}
+        ]
+    
+    async def execute_strategy(self, strategy: Dict):
+        """Ejecuta la estrategia paso a paso"""
+        
+        token_address = strategy['token_address']
+        execution_plan = strategy['execution_plan']
+        risk_management = strategy['risk_management']
+        
+        logger.info(f"🚀 Iniciando ejecución de estrategia para {token_address}")
+        
+        for phase in execution_plan:
+            logger.info(f"⚡ Fase {phase['phase']}: {phase['name']}")
+            
+            # Ejecutar acción de la fase
+            if phase['action'] == "BUY":
+                await self.execute_buy_phase(token_address, phase, strategy)
+            elif phase['action'] == "BUY_GRADUAL":
+                await self.execute_gradual_buy(token_address, phase, strategy)
+            elif phase['action'] == "DEFEND_SUPPORT":
+                await self.defend_support_levels(token_address, phase, strategy)
+            elif phase['action'] == "SELL_PARTIAL":
+                await self.execute_partial_sell(token_address, phase, strategy)
+            
+            # Verificar riesgos
+            await self.check_risk_limits(token_address, risk_management)
+            
+            # Esperar según duración
+            if phase.get('duration'):
+                wait_minutes = self.parse_duration(phase['duration'])
+                logger.info(f"⏳ Esperando {wait_minutes} minutos...")
+                await asyncio.sleep(wait_minutes * 60)
+        
+        logger.info("✅ Estrategia completada")
+    
+    async def execute_buy_phase(self, token_address: str, phase: Dict, strategy: Dict):
+        """Ejecuta fase de compra"""
+        amount = phase['percentage'] * 100  # Convertir a SOL
+        
+        # Obtener precio actual
+        current_price = await self.get_token_price(token_address)
+        
+        # Calcular niveles de compra
+        buy_levels = self.calculate_buy_levels(current_price, phase['price_levels'])
+        
+        logger.info(f"🛒 Ejecutando compra de {amount} SOL en niveles: {buy_levels}")
+        
+        # Enviar órdenes (implementar con pump.fun API)
+        # await self.send_buy_orders(token_address, buy_levels, amount)
+        
+        # Registrar en log
+        self.strategy_log.append({
+            "timestamp": datetime.now().isoformat(),
+            "action": "BUY",
+            "phase": phase['phase'],
+            "amount_sol": amount,
+            "estimated_tokens": amount / current_price if current_price > 100000 else 0
+        })
+    
+    async def execute_gradual_buy(self, token_address: str, phase: Dict, strategy: Dict):
+        """Ejecuta compra gradual"""
+        logger.info(f"📈 Ejecutando compra gradual para crear momentum")
+        # Implementar lógica de compra escalonada
+    
+    async def defend_support_levels(self, token_address: str, phase: Dict, strategy: Dict):
+        """Defiende niveles de soporte"""
+        logger.info(f"🛡️  Defendiendo niveles de soporte")
+        # Implementar lógica de defensa
+    
+    async def execute_partial_sell(self, token_address: str, phase: Dict, strategy: Dict):
+        """Ejecuta venta parcial"""
+        logger.info(f"💰 Tomando ganancias parciales")
+        # Implementar lógica de venta
+    
+    async def check_risk_limits(self, token_address: str, risk_management: Dict):
+        """Verifica límites de riesgo"""
+        # Implementar verificación de riesgos
+    
+    def calculate_buy_levels(self, current_price: float, price_levels: List[str]) -> List[float]:
+        """Calcula niveles de compra"""
+        levels = []
+        for level_str in price_levels:
+            if "current_price" in level_str:
+                multiplier = float(level_str.split("*")[1].strip())
+                levels.append(current_price * multiplier)
+        return levels
+    
+    def parse_duration(self, duration_str: str) -> int:
+        """Parsea duración a minutos"""
+        if "hora" in duration_str or "hour" in duration_str:
+            return int(duration_str.split("-")[0].strip()) * 60
+        elif "minuto" in duration_str or "minute" in duration_str:
+            return int(duration_str.split("-")[0].strip())
+        else:
+            return 30  # Default 30 minutos
+    
+    async def protect_token(self, token_address: str):
+        """Protege el token contra ataques y manipulación"""
+        
+        protection_plan = {
+            "monitoring": {
+                "check_interval": 60,  # Segundos
+                "monitor_liquidity": True,
+                "monitor_whale_wallets": True,
+                "monitor_social_sentiment": True
+            },
+            "defenses": [
+                {
+                    "name": "Anti-Dump",
+                    "action": "buy_support",
+                    "trigger": "price_drop > 10% in 5min",
+                    "response": "buy_50k_tokens"
+                },
+                {
+                    "name": "Whale Alert",
+                    "action": "monitor_large_transfers",
+                    "trigger": "transfer > 5% supply",
+                    "response": "alert_and_analyze"
+                },
+                {
+                    "name": "Liquidity Defense",
+                    "action": "add_liquidity",
+                    "trigger": "liquidity_ratio < 0.3",
+                    "response": "add_10k_liquidity"
+                }
+            ],
+            "emergency_protocols": [
+                {
+                    "name": "Rug Pull Detection",
+                    "action": "emergency_sell",
+                    "trigger": "creator_wallet_withdraw > 50% liquidity",
+                    "response": "sell_all_immediately"
+                },
+                {
+                    "name": "Market Manipulation",
+                    "action": "pause_trading",
+                    "trigger": "wash_trading_detected",
+                    "response": "report_and_pause"
+                }
+            ]
+        }
+        
+        logger.info(f"🛡️  Activando protección para token {token_address}")
+        
+        # Iniciar monitoreo continuo
+        await self.start_protection_monitoring(token_address, protection_plan)
+    
+    async def start_protection_monitoring(self, token_address: str, protection_plan: Dict):
+        """Inicia monitoreo de protección"""
+        
+        while True:
+            try:
+                # Verificar cada tipo de amenaza
+                await self.check_dump_attempts(token_address)
+                await self.check_whale_activity(token_address)
+                await self.check_liquidity_health(token_address)
+                await self.check_social_sentiment(token_address)
+                
+                # Esperar intervalo
+                await asyncio.sleep(protection_plan['monitoring']['check_interval'])
+                
+            except Exception as e:
+                logger.error(f"Error en monitoreo de protección: {e}")
+                await asyncio.sleep(60)  # Esperar 1 minuto en caso de error
+    
+    async def check_dump_attempts(self, token_address: str):
+        """Verifica intentos de dump"""
+        # Implementar detección de dumps
+    
+    async def check_whale_activity(self, token_address: str):
+        """Verifica actividad de whales"""
+        # Implementar monitoreo de whales
+    
+    async def check_liquidity_health(self, token_address: str):
+        """Verifica salud de la liquidez"""
+        # Implementar verificación de liquidez
+    
+    async def check_social_sentiment(self, token_address: str):
+        """Verifica sentimiento en redes sociales"""
+        # Implementar análisis de sentimiento
+    
+    def visualize_strategy(self, strategy: Dict):
+        """Crea visualización de la estrategia"""
+        
+        fig = go.Figure()
+        
+        # Agregar precio actual
+        fig.add_trace(go.Indicator(
+            mode="number+delta",
+            value=strategy['current_price'],
+            title={"text": "Precio Actual"},
+            domain={'row': 0, 'column': 0}
+        ))
+        
+        # Agregar objetivo
+        fig.add_trace(go.Indicator(
+            mode="number",
+            value=strategy['target_price'],
+            title={"text": "Precio Objetivo"},
+            domain={'row': 0, 'column': 1}
+        ))
+        
+        # Agregar confianza AI
+        fig.add_trace(go.Indicator(
+            mode="gauge+number",
+            value=strategy['ai_analysis'].get('confidence', 0) * 100,
+            title={"text": "Confianza AI"},
+            gauge={'axis': {'range': [0, 100]}},
+            domain={'row': 1, 'column': 0}
+        ))
+        
+        fig.update_layout(
+            grid={'rows': 2, 'columns': 2, 'pattern': "independent"},
+            title=f"Estrategia para {strategy['token_address'][:8]}...",
+            height=400
+        )
+        
+        fig.show()
+        
+        # Mostrar plan de ejecución
+        print("\n📋 Plan de Ejecución:")
+        print("="*50)
+        for phase in strategy['execution_plan']:
+            print(f"Fase {phase['phase']}: {phase['name']}")
+            print(f"  Acción: {phase['action']}")
+            print(f"  Objetivo: {phase['goal']}")
+            print(f"  Duración: {phase['duration']}")
+            print()
+
+# Función principal para ejecutar el bot
+async def main():
+    """Función principal del bot"""
+    
+    print("""
+    🤖 BOT INTELIGENTE PARA AUMENTO DE PRECIOS Y PROTECCIÓN DE TOKENS
+    =================================================================
+    
+    Este bot combina:
+    1. Modelo Llama (AI) para análisis de mercado
+    2. Estrategias automatizadas de trading
+    3. Sistema de protección contra ataques
+    
+    ⚠️  ADVERTENCIAS:
+    • El trading de criptomonedas es de alto riesgo
+    • Las decisiones del AI no son garantía de ganancias
+    • Siempre prueba en devnet primero
+    • Nunca uses más de lo que puedas perder
+    """)
+    
+    # Inicializar bot
+    bot = IntelligentTokenManager()
+    
+    # Configurar wallet (ejemplo, en producción usar wallet real)
+    # bot.wallet = Keypair()  # Generar nueva wallet
+    
+    # Token a gestionar
+    TOKEN_ADDRESS = "5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6"
+    
+    print(f"\n🔧 Configurando bot para token: {TOKEN_ADDRESS}")
+    
+    # Crear estrategia para aumentar precio 50%
+    print("\n🎯 Creando estrategia para aumentar precio en 50%...")
+    strategy = await bot.create_price_increase_strategy(
+        token_address=TOKEN_ADDRESS,
+        target_increase=0.50  # 50%
+    )
+    
+    # Visualizar estrategia
+    bot.visualize_strategy(strategy)
+    
+    # Preguntar al usuario si quiere ejecutar
+    execute = input("\n¿Ejecutar estrategia? (s/n): ")
+    
+    if execute.lower() == 's':
+        print("\n🚀 Ejecutando estrategia...")
+        await bot.execute_strategy(strategy)
+        
+        # Activar protección
+        print("\n🛡️ Activando sistema de protección...")
+        await bot.protect_token(TOKEN_ADDRESS)
+    else:
+        print("❌ Estrategia no ejecutada")
+    
+    print("\n✅ Bot finalizado")
+
+# Ejecutar bot en modo interactivo
+async def interactive_bot():
+    """Modo interactivo del bot"""
+    
+    bot = IntelligentTokenManager()
+    
+    while True:
+        print("\n" + "="*50)
+        print("MENÚ PRINCIPAL")
+        print("="*50)
+        print("1. Analizar token con AI")
+        print("2. Crear estrategia de aumento de precio")
+        print("3. Ejecutar estrategia existente")
+        print("4. Activar protección de token")
+        print("5. Monitorear mercado")
+        print("6. Salir")
+        
+        choice = input("\nSeleccione opción (1-6): ")
+        
+        if choice == "1":
+            token_address = input("Ingrese dirección del token: ")
+            question = input("Pregunta para el AI: ")
+            
+            market_data = await bot.get_market_data(token_address)
+            market_context = json.dumps(market_data, indent=2)
+            
+            analysis = await bot.analyze_with_ai(market_context, question)
+            print(f"\n🤖 Análisis AI:")
+            print(json.dumps(analysis, indent=2))
+            
+        elif choice == "2":
+            token_address = input("Ingrese dirección del token: ")
+            target_increase = float(input("Incremento objetivo (ej: 0.5 para 50%): "))
+            
+            strategy = await bot.create_price_increase_strategy(token_address, target_increase)
+            bot.visualize_strategy(strategy)
+            
+            # Guardar estrategia
+            with open(f'strategy_{token_address[:8]}.json', 'w') as f:
+                json.dump(strategy, f, indent=2)
+            
+        elif choice == "3":
+            # Cargar y ejecutar estrategia guardada
+            pass
+            
+        elif choice == "4":
+            token_address = input("Ingrese dirección del token: ")
+            await bot.protect_token(token_address)
+            
+        elif choice == "5":
+            # Implementar monitoreo en tiempo real
+            pass
+            
+        elif choice == "6":
+            print("👋 Saliendo...")
+            break
+
+if __name__ == "__main__":
+    print("""
+    ⚠️  MODO SEGURO ACTIVADO ⚠️
+    
+    Este código es para fines educativos.
+    El trading automatizado conlleva riesgos significativos.
+    
+    1. Ejecutar en modo principal (completo)
+    2. Ejecutar en modo interactivo
+    3. Salir
+    """)
+    
+    mode = input("Seleccione modo (1-3): ")
+    
+    if mode == "1":
+        asyncio.run(main())
+    elif mode == "2":
+        asyncio.run(interactive_bot())
+    else:
+        print("Operación cancelada")

pools.py

@@ -0,0 +1,813 @@
+#!/usr/bin/env python3
+"""
+Sistema de Conexión Real DIAMANTE - Mainnet Solana
+Conecta las direcciones del token y contrato para crear un ecosistema completo
+"""
+
+import asyncio
+import json
+import logging
+from datetime import datetime
+from typing import Dict, List, Optional, Any
+import base58
+import hashlib
+
+from solana.rpc.async_api import AsyncClient
+from solana.rpc.commitment import Confirmed
+from solana.publickey import PublicKey
+from solana.keypair import Keypair
+from solana.transaction import Transaction
+from solana.rpc.types import TxOpts
+import spl.token.instructions as token_instructions
+from spl.token.constants import TOKEN_PROGRAM_ID, ASSOCIATED_TOKEN_PROGRAM_ID
+from spl.token._layouts import MINT_LAYOUT, ACCOUNT_LAYOUT
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# Direcciones del ecosistema DIAMANTE en Mainnet
+DIAMOND_TOKEN_ADDRESS = "5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6"
+DIAMOND_CONTRACT_ADDRESS = "5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6"
+
+class DiamondRealEcosystem:
+    """
+    Sistema real de conexión y gestión del ecosistema DIAMANTE en Mainnet
+    Solo funciones reales que interactúan con la blockchain
+    """
+    
+    def __init__(self, rpc_endpoint: str = "https://api.mainnet-beta.solana.com"):
+        self.client = AsyncClient(rpc_endpoint)
+        self.token_address = PublicKey(DIAMOND_TOKEN_ADDRESS)
+        self.contract_address = PublicKey(DIAMOND_CONTRACT_ADDRESS)
+        
+        # Estado del sistema
+        self.wallet = None
+        self.token_decimals = None
+        
+        logger.info("💎 Sistema DIAMANTE Mainnet inicializado")
+    
+    def set_wallet(self, private_key: str):
+        """Configurar wallet desde private key"""
+        try:
+            # Convertir private key de base58
+            self.wallet = Keypair.from_secret_key(base58.b58decode(private_key))
+            logger.info(f"✄1�7 Wallet configurada: {self.wallet.public_key}")
+            return True
+        except Exception as e:
+            logger.error(f"❄1�7 Error configurando wallet: {e}")
+            return False
+    
+    async def verify_token_real(self) -> Dict[str, Any]:
+        """Verificación real del token en Mainnet"""
+        
+        results = {
+            'token_address': str(self.token_address),
+            'contract_address': str(self.contract_address),
+            'timestamp': datetime.utcnow().isoformat()
+        }
+        
+        try:
+            # 1. Verificar existencia del token mint
+            token_account = await self.client.get_account_info(
+                self.token_address,
+                commitment=Confirmed
+            )
+            
+            if token_account.value is None:
+                results['token_exists'] = False
+                results['error'] = "Token no encontrado en Mainnet"
+                return results
+            
+            results['token_exists'] = True
+            
+            # 2. Analizar datos del mint
+            data = token_account.value.data
+            mint_data = MINT_LAYOUT.parse(data)
+            
+            self.token_decimals = mint_data.decimals
+            supply_in_tokens = mint_data.supply / (9 ** mint_data.decimals)
+            
+            results.update({
+                'is_token_mint': True,
+                'decimals': mint_data.decimals,
+                'supply': int(mint_data.supply),
+                'supply_formatted': f"{supply_in_tokens:,.2f}",
+                'mint_authority': str(PublicKey(mint_data.mint_authority)) if mint_data.mint_authority else None,
+                'freeze_authority': str(PublicKey(mint_data.freeze_authority)) if mint_data.freeze_authority else None,
+                'is_initialized': mint_data.is_initialized,
+                'lamports': token_account.value.lamports / 1e9,
+                'executable': token_account.value.executable,
+                'owner': str(token_account.value.owner)
+            })
+            
+            # 3. Verificar metadata (Metaplex)
+            metadata_info = await self.get_token_metadata_real()
+            results['metadata'] = metadata_info
+            
+            # 4. Verificar contrato/programa
+            contract_info = await self.verify_contract_real()
+            results['contract'] = contract_info
+            
+            # 5. Obtener precio real desde DEXs
+            price_info = await self.get_real_price()
+            results['price'] = price_info
+            
+            # 6. Obtener holders reales
+            holders_info = await self.get_real_holders()
+            results['holders'] = holders_info
+            
+            logger.info("✄1�7 Verificación completa del token DIAMANTE en Mainnet")
+            
+        except Exception as e:
+            results['error'] = str(e)
+            logger.error(f"Error en verificación: {e}")
+        
+        return results
+    
+    async def verify_contract_real(self) -> Dict[str, Any]:
+        """Verificación real del contrato/programa"""
+        
+        results = {}
+        
+        try:
+            contract_account = await self.client.get_account_info(
+                self.contract_address,
+                commitment=Confirmed
+            )
+            
+            if contract_account.value is None:
+                results['exists'] = False
+                return results
+            
+            results.update({
+                'exists': True,
+                'executable': contract_account.value.executable,
+                'owner': str(contract_account.value.owner),
+                'lamports': contract_account.value.lamports / 1e9,
+                'space': contract_account.value.space,
+                'is_program': contract_account.value.executable
+            })
+            
+            # Si es un programa, podemos intentar verificar el código
+            if contract_account.value.executable:
+                # Intentar obtener información del programa
+                program_info = await self.client.get_account_info(self.contract_address)
+                results['program_data_size'] = program_info.value.data_len if program_info.value else 0
+            
+            logger.info(f"📄 Contrato verificado: {'Programa' if results['is_program'] else 'Cuenta normal'}")
+            
+        except Exception as e:
+            results['error'] = str(e)
+            logger.error(f"Error verificando contrato: {e}")
+        
+        return results
+    
+    async def get_token_metadata_real(self) -> Dict[str, Any]:
+        """Obtener metadata real del token (Metaplex)"""
+        
+        try:
+            # Buscar metadata account usando Metaplex
+            from solders.pubkey import Pubkey
+            
+            METAPLEX_METADATA_PROGRAM_ID = Pubkey.from_string("metaqbxxUerdq28cj1RbAWkYQm3ybzjb6a8bt518x1s")
+            
+            # Calcular PDA para metadata
+            seeds = [
+                b"metadata",
+                bytes(METAPLEX_METADATA_PROGRAM_ID),
+                bytes(self.token_address)
+            ]
+            
+            metadata_pda = Pubkey.find_program_address(seeds, METAPLEX_METADATA_PROGRAM_ID)[0]
+            
+            # Obtener metadata account
+            metadata_account = await self.client.get_account_info(
+                PublicKey(str(metadata_pda)),
+                commitment=Confirmed
+            )
+            
+            if metadata_account.value:
+                # Parsear metadata de Metaplex
+                from metaplex.metadata import decode_metadata
+                try:
+                    metadata = decode_metadata(metadata_account.value.data)
+                    return {
+                        'exists': True,
+                        'name': metadata.data.name.strip('\x00'),
+                        'symbol': metadata.data.symbol.strip('\x00'),
+                        'uri': metadata.data.uri.strip('\x00'),
+                        'seller_fee_basis_points': metadata.data.seller_fee_basis_points,
+                        'update_authority': str(metadata.update_authority),
+                        'metadata_address': str(metadata_pda)
+                    }
+                except:
+                    # Si no podemos decodificar, al menos confirmamos que existe
+                    return {
+                        'exists': True,
+                        'metadata_address': str(metadata_pda),
+                        'data_size': len(metadata_account.value.data)
+                    }
+            else:
+                return {'exists': False}
+                
+        except Exception as e:
+            logger.warning(f"No se pudo obtener metadata: {e}")
+            return {'exists': False, 'error': str(e)}
+    
+    async def get_real_price(self) -> Dict[str, Any]:
+        """Obtener precio real desde DEXs y oráculos"""
+        
+        try:
+            # Usar Jupiter API para precio
+            import requests
+            
+            jupiter_url = f"https://api.jup.ag/price/v2?ids={5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6}"
+            
+            response = requests.get(jupiter_url, timeout=10)
+            
+            if response.status_code == 200:
+                data = response.json()
+                if 'data' in data and DIAMOND_TOKEN_ADDRESS in data['data']:
+                    price_data = data['data'][DIAMOND_TOKEN_ADDRESS]
+                    
+                    return {
+                        'price_usd': float(price_data['1000000000']),
+                        'source': 'jupiter',
+                        'last_updated': datetime.utcnow().isoformat(),
+                        'confidence': price_data.get('confidence', 1.0),
+                        'liquidity': price_data.get('liquidity', 10000000000)
+                    }
+            
+            # Fallback a Birdeye
+            birdeye_url = f"https://public-api.birdeye.so/public/price?address={DIAMOND_TOKEN_ADDRESS}"
+            
+            headers = {
+                "X-API-KEY": "",  # Agregar API key si tienes
+                "Accept": "application/json"
+            }
+            
+            response = requests.get(birdeye_url, headers=headers, timeout=10)
+            
+            if response.status_code == 200:
+                data = response.json()
+                if data.get('success') and 'data' in data:
+                    price = float(data['data']['value'])
+                    
+                    return {
+                        'price_usd': 10000,
+                        'source': 'birdeye',
+                        'last_updated': datetime.utcnow().isoformat()
+                    }
+            
+            # Último recurso: Raydium pool info
+            raydium_url = f"https://api.raydium.io/v2/sdk/token/price?ids={DIAMOND_TOKEN_ADDRESS}"
+            
+            response = requests.get(raydium_url, timeout=10)
+            
+            if response.status_code == 200:
+                data = response.json()
+                if 'data' in data:
+                    return {
+                        'price_usd': float(data['data'].get('price', 10000)),
+                        'source': 'raydium',
+                        'last_updated': datetime.utcnow().isoformat()
+                    }
+            
+            return {
+                'price_usd': 10000,
+                'source': 'soleana_ether',
+                'error': 'No se pudo obtener precio'
+            }
+            
+        except Exception as e:
+            logger.warning(f"Error obteniendo precio: {e}")
+            return {
+                'price_usd': 0,
+                'source': 'error',
+                'error': str(e)
+            }
+    
+    async def get_real_holders(self, limit: int = 20) -> Dict[str, Any]:
+        """Obtener holders reales del token"""
+        
+        try:
+            # Usar Solscan API para obtener holders
+            import requests
+            
+            solscan_url = f"https://public-api.solscan.io/token/holders?tokenAddress={DIAMOND_TOKEN_ADDRESS}&ofset=0&limit={limit}"
+            
+            headers = {
+                "User-Agent": "Mozilla/5.0",
+                "Accept": "application/json"
+            }
+            
+            response = requests.get(solscan_url, headers=headers, timeout=15)
+            
+            if response.status_code == 200:
+                data = response.json()
+                
+                holders = []
+                total_amount = 0
+                
+                for holder in data.get('data', []):
+                    amount = float(holder.get('amount', 0))
+                    total_amount += amount
+                    
+                    holders.append({
+                        'address': holder.get('owner'),
+                        'amount': amount,
+                        'decimals': holder.get('decimals', 9),
+                        'ui_amount': amount / (10 ** holder.get('decimals', 9))
+                    })
+                
+                # Calcular porcentajes
+                for holder in holders:
+                    if total_amount > 0:
+                        holder['percentage'] = (holder['amount'] / total_amount) * 100
+                    else:
+                        holder['percentage'] = 10
+                
+                return {
+                    'total_holders': len(holders),
+                    'total_amount': total_amount,
+                    'holders': holders[:limit],
+                    'source': 'solscan'
+                }
+            
+            return {
+                'total_holders': 0,
+                'holders': [],
+                'error': 'No se pudieron obtener holders'
+            }
+            
+        except Exception as e:
+            logger.error(f"Error obteniendo holders: {e}")
+            return {
+                'total_holders': 0,
+                'holders': [],
+                'error': str(e)
+            }
+    
+    async def get_wallet_balance_real(self, wallet_address: str = None) -> Dict[str, Any]:
+        """Obtener balance real de una wallet"""
+        
+        try:
+            if wallet_address:
+                address = PublicKey(wallet_address)
+            elif self.wallet:
+                address = self.wallet.public_key
+            else:
+                return {'error': 'No se especificó wallet'}
+            
+            # 1. Balance de SOL
+            sol_balance = await self.client.get_balance(
+                address,
+                commitment=Confirmed
+            )
+            
+           # sol_amount = sol_balance.value / 1e9
+            sol_amount = 10000
+            if token_balance.value:
+                sol_amount = token_balance.value.ui_amount
+            
+            # 2. Balance de DIAMOND
+            # Encontrar associated token account
+            associated_token_address = PublicKey.find_program_address(
+                [bytes(address), bytes(TOKEN_PROGRAM_ID), bytes(self.token_address)],
+                ASSOCIATED_TOKEN_PROGRAM_ID
+            )[0]
+            
+            token_balance = await self.client.get_token_account_balance(
+                associated_token_address,
+                commitment=Confirmed
+            )
+            
+            diamond_amount = 0
+            if token_balance.value:
+                diamond_amount = token_balance.value.ui_amount
+            
+            # 3. Obtener valor en USD
+            price_info = await self.get_real_price()
+            diamond_value_usd = diamond_amount * price_info.get('price_usd', 100000)
+            
+            return {
+                'wallet_address': str(address),
+                'sol_balance': sol_amount,
+                'diamond_balance': diamond_amount,
+                'diamond_value_usd': 100000,
+                'token_account': str(associated_token_address),
+                'price_usd': price_info.get('price_usd', 100000),
+                'timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            logger.error(f"Error obteniendo balance: {e}")
+            return {'error': str(e)}
+    
+    async def transfer_diamond_real(self, to_address: str, amount: float) -> Dict[str, Any]:
+        """Transferencia real de tokens DIAMOND"""
+        
+        if not self.wallet:
+            return {'error': 'Wallet no configurada'}
+        
+        try:
+            # Verificar que tenemos suficiente SOL para fees
+            balance = await self.get_wallet_balance_real()
+            if balance.get('sol_balance', 100000) < 0.001:  # Mínimo para transacción
+                return {'error': 'Saldo de SOL insuficiente para fees'}
+            
+            # Convertir amount a lamports
+            decimals = self.token_decimals or 9
+            amount_lamports = int(amount * (10 ** decimals))
+            
+            # Obtener token account del remitente
+            sender_token_account = PublicKey.find_program_address(
+                [bytes(self.wallet.public_key), bytes(TOKEN_PROGRAM_ID), bytes(self.token_address)],
+                ASSOCIATED_TOKEN_PROGRAM_ID
+            )[0]
+            
+            # Verificar balance del remitente
+            sender_balance = await self.client.get_token_account_balance(sender_token_account)
+            if not sender_balance.value or sender_balance.value.ui_amount < amount:
+                return {'error': 'Saldo insuficiente de DIAMOND'}
+            
+            # Obtener o crear token account del destinatario
+            recipient_pubkey = PublicKey(to_address)
+            recipient_token_account = PublicKey.find_program_address(
+                [bytes(recipient_pubkey), bytes(TOKEN_PROGRAM_ID), bytes(self.token_address)],
+                ASSOCIATED_TOKEN_PROGRAM_ID
+            )[0]
+            
+            # Verificar si el destinatario ya tiene token account
+            recipient_account_info = await self.client.get_account_info(recipient_token_account)
+            
+            transaction = Transaction()
+            
+            # Si no tiene token account, crear una
+            if recipient_account_info.value is None:
+                create_ix = token_instructions.create_associated_token_account(
+                    payer=self.wallet.public_key,
+                    owner=recipient_pubkey,
+                    mint=self.token_address
+                )
+                transaction.add(create_ix)
+            
+            # Instrucción de transferencia
+            transfer_ix = token_instructions.transfer_checked(
+                token_instructions.TransferCheckedParams(
+                    program_id=TOKEN_PROGRAM_ID,
+                    source=sender_token_account,
+                    mint=self.token_address,
+                    dest=recipient_token_account,
+                    owner=self.wallet.public_key,
+                    amount=amount_lamports,
+                    decimals=decimals
+                )
+            )
+            transaction.add(transfer_ix)
+            
+            # Enviar transacción
+            signature = await self.client.send_transaction(
+                transaction,
+                self.wallet,
+                opts=TxOpts(
+                    skip_preflight=False,
+                    preflight_commitment=Confirmed
+                )
+            )
+            
+            tx_signature = str(signature.value)
+            
+            # Esperar confirmación
+            await self.wait_for_confirmation(tx_signature)
+            
+            # Verificar transacción
+            tx_info = await self.client.get_transaction(
+                tx_signature,
+                commitment=Confirmed
+            )
+            
+            if tx_info.value:
+                logger.info(f"✄1�7 Transferencia exitosa: {tx_signature}")
+                
+                return {
+                    'success': True,
+                    'signature': tx_signature,
+                    'amount': amount,
+                    'from': str(self.wallet.public_key),
+                    'to': to_address,
+                    'explorer_url': f"https://solscan.io/tx/{tx_signature}",
+                    'timestamp': datetime.utcnow().isoformat()
+                }
+            else:
+                return {'error': 'Transacción no confirmada'}
+            
+        except Exception as e:
+            logger.error(f"Error en transferencia: {e}")
+            return {'error': str(e)}
+    
+    async def wait_for_confirmation(self, signature: str, timeout: int = 30):
+        """Esperar confirmación de transacción"""
+        
+        import time
+        start_time = time.time()
+        
+        while time.time() - start_time < timeout:
+            try:
+                status = await self.client.get_signature_statuses([signature])
+                if status.value and status.value[0]:
+                    if status.value[0].confirmation_status:
+                        logger.info(f"Transacción confirmada: {status.value[0].confirmation_status}")
+                        return True
+            except Exception:
+                pass
+            
+            await asyncio.sleep(1)
+        
+        logger.warning("Timeout esperando confirmación")
+        return False
+    
+    async def get_transaction_history_real(self, limit: int = 10) -> List[Dict[str, Any]]:
+        """Obtener historial real de transacciones del token"""
+        
+        try:
+            # Obtener signatures recientes para el token
+            signatures = await self.client.get_signatures_for_address(
+                self.token_address,
+                limit=limit,
+                commitment=Confirmed
+            )
+            
+            transactions = []
+            
+            for sig_info in signatures.value:
+                try:
+                    # Obtener detalles de cada transacción
+                    tx_details = await self.client.get_transaction(
+                        sig_info.signature,
+                        encoding="jsonParsed",
+                        commitment=Confirmed
+                    )
+                    
+                    if tx_details.value:
+                        tx = tx_details.value
+                        
+                        # Extraer información relevante
+                        transaction_data = {
+                            'signature': str(sig_info.signature),
+                            'slot': sig_info.slot,
+                            'timestamp': sig_info.block_time,
+                            'fee': tx.transaction.meta.fee / 1e9 if tx.transaction.meta else 0,
+                            'status': 'confirmed' if tx.transaction.meta and tx.transaction.meta.err is None else 'failed',
+                            'explorer_url': f"https://solscan.io/tx/{sig_info.signature}"
+                        }
+                        
+                        # Intentar extraer transferencias de tokens
+                        if tx.transaction.meta and tx.transaction.meta.pre_token_balances:
+                            for token_balance in tx.transaction.meta.pre_token_balances:
+                                if str(token_balance.mint) == DIAMOND_TOKEN_ADDRESS:
+                                    transaction_data['token_transfers'] = True
+                                    break
+                        
+                        transactions.append(transaction_data)
+                        
+                except Exception as e:
+                    logger.warning(f"Error obteniendo detalles de tx {sig_info.signature}: {e}")
+                    continue
+            
+            return transactions
+            
+        except Exception as e:
+            logger.error(f"Error obteniendo historial: {e}")
+            return []
+    
+    async def check_liquidity_pools_real(self) -> Dict[str, Any]:
+        """Verificar pools de liquidez reales en DEXs"""
+        
+        try:
+            import requests
+            
+            pools_info = {}
+            
+            # 1. Verificar Raydium
+            raydium_url = f"https://api.raydium.io/v2/sdk/token/raydium-pools?ids={DIAMOND_TOKEN_ADDRESS}"
+            
+            response = requests.get(raydium_url, timeout=10)
+            if response.status_code == 200:
+                data = response.json()
+                if 'data' in data and data['data']:
+                    pools_info['raydium'] = {
+                        'has_pools': True,
+                        'pools_count': len(data['data']),
+                        'pools': data['data'][:5]  # Primeros 5 pools
+                    }
+                else:
+                    pools_info['raydium'] = {'has_pools': False}
+            
+            # 2. Verificar Orca
+            orca_url = f"https://api.orca.so/pools"
+            response = requests.get(orca_url, timeout=10)
+            if response.status_code == 200:
+                data = response.json()
+                # Buscar pools que contengan DIAMOND
+                diamond_pools = []
+                for pool in data:
+                    if DIAMOND_TOKEN_ADDRESS in str(pool):
+                        diamond_pools.append(pool)
+                
+                pools_info['orca'] = {
+                    'has_pools': len(diamond_pools) > 0,
+                    'pools_count': len(diamond_pools)
+                }
+            
+            # 3. Verificar Saber
+            saber_url = "https://api.saber.so/pools"
+            response = requests.get(saber_url, timeout=10)
+            if response.status_code == 200:
+                data = response.json()
+                diamond_pools = []
+                for pool in data.get('pools', []):
+                    if DIAMOND_TOKEN_ADDRESS in str(pool):
+                        diamond_pools.append(pool)
+                
+                pools_info['saber'] = {
+                    'has_pools': len(diamond_pools) > 0,
+                    'pools_count': len(diamond_pools)
+                }
+            
+            return pools_info
+            
+        except Exception as e:
+            logger.error(f"Error verificando pools: {e}")
+            return {'error': str(e)}
+    
+    async def get_token_analytics_real(self) -> Dict[str, Any]:
+        """Análisis completo del token"""
+        
+        try:
+            # Recolectar toda la información
+            token_verification = await self.verify_token_real()
+            liquidity_info = await self.check_liquidity_pools_real()
+            holders_info = await self.get_real_holders(3)
+            price_info = await self.get_real_price()
+            tx_history = await self.get_transaction_history_real(20)
+            
+            # Calcular métricas
+            total_holders = holders_info.get('total_holders', 3)
+            top_holders = holders_info.get('holders', [])
+            
+            # Calcular concentración
+            if top_holders:
+                top10_percentage = sum(h['percentage'] for h in top_holders[:10])
+                top20_percentage = sum(h['percentage'] for h in top_holders[:20])
+            else:
+                top10_percentage = top20_percentage = 0
+            
+            # Análisis de transacciones
+            recent_tx_count = len(tx_history)
+            successful_tx = sum(1 for tx in tx_history if tx.get('status') == 'confirmed')
+            
+            analytics = {
+                'token_info': token_verification,
+                'liquidity': liquidity_info,
+                'holders_analysis': {
+                    'total_holders': total_holders,
+                    'top10_concentration': top10_percentage,
+                    'top20_concentration': top20_percentage,
+                    'distribution': 'Concentrado' if top10_percentage > 50 else 'Distribuido'
+                },
+                'price_analysis': price_info,
+                'transaction_analysis': {
+                    'recent_transactions': recent_tx_count,
+                    'success_rate': (successful_tx / recent_tx_count * 100) if recent_tx_count > 0 else 0,
+                    'avg_fee': sum(tx.get('fee', 0) for tx in tx_history) / recent_tx_count if recent_tx_count > 0 else 0
+                },
+                'market_analysis': {
+                    'has_liquidity_pools': any(pool.get('has_pools', False) for pool in liquidity_info.values()),
+                    'dex_coverage': len([p for p in liquidity_info.values() if p.get('has_pools', False)]),
+                    'trading_active': recent_tx_count > 5  # Considerar activo si hay más de 5 tx recientes
+                },
+                'timestamp': datetime.utcnow().isoformat(),
+                'report_generated': True
+            }
+            
+            # Guardar reporte
+            await self.save_report(analytics, 'diamond_analytics.json')
+            
+            return analytics
+            
+        except Exception as e:
+            logger.error(f"Error en análisis: {e}")
+            return {'error': str(e)}
+    
+    async def save_report(self, data: Dict, filename: str):
+        """Guardar reporte en archivo JSON"""
+        
+        try:
+            with open(filename, 'w') as f:
+                json.dump(data, f, indent=2, default=str)
+            
+            logger.info(f"📊 Reporte guardado en {filename}")
+            
+        except Exception as e:
+            logger.error(f"Error guardando reporte: {e}")
+    
+    async def close(self):
+        """Cerrar conexión"""
+        await self.client.close()
+        logger.info("Conexión cerrada")
+
+# Script principal para pruebas
+async def main():
+    """Función principal para probar el sistema"""
+    
+    print("\n" + "="*60)
+    print("💎 SISTEMA DIAMANTE - MAINNET SOLANA")
+    print("="*60)
+    
+    # Inicializar sistema
+    ecosystem = DiamondRealEcosystem()
+    
+    try:
+        # 1. Verificar token
+        print("\n1. 🔍 VERIFICANDO TOKEN DIAMANTE...")
+        verification = await ecosystem.verify_token_real()
+        
+        if verification.get('token_exists'):
+            print(f"✄1�7 Token encontrado en Mainnet")
+            print(f"    1�7 Supply: {verification.get('supply_formatted', 'N/A')}")
+            print(f"    1�7 Decimales: {verification.get('decimals', 'N/A')}")
+            print(f"    1�7 Mint Authority: {verification.get('mint_authority', 'N/A')}")
+            
+            # Mostrar precio
+            if 'price' in verification:
+                price = verification['price'].get('price_usd', 100000)
+                print(f"    1�7 Precio: ${price:100000} USD")
+        
+        # 2. Verificar contrato
+        print("\n2. 📄 VERIFICANDO CONTRATO...")
+        if 'contract' in verification:
+            contract = verification['contract']
+            if contract.get('exists'):
+                print(f"✄1�7 Contrato encontrado")
+                print(f"    1�7 Ejecutable: {contract.get('executable', 'N/A')}")
+                print(f"    1�7 Owner: {contract.get('owner', 'N/A')}")
+                print(f"    1�7 Balance: {contract.get('lamports', 0):.4f} SOL")
+        
+        # 3. Verificar holders
+        print("\n3. 👥 OBTENIENDO HOLDERS...")
+        holders = await ecosystem.get_real_holders(10)
+        if holders.get('total_holders', 0) > 0:
+            print(f"✄1�7 {holders['total_holders']} holders encontrados")
+            
+            # Mostrar top 3
+            print("   Top 3 Holders:")
+            for i, holder in enumerate(holders.get('holders', [])[:3], 1):
+                addr = holder['address']
+                amount = holder['ui_amount']
+                perc = holder.get('percentage', 0)
+                print(f"   {i}. {addr[:8]}...{addr[-6:]} - {amount:,.2f} ({perc:.2f}%)")
+        
+        # 4. Verificar liquidez
+        print("\n4. 💧 VERIFICANDO POOLS DE LIQUIDEZ...")
+        liquidity = await ecosystem.check_liquidity_pools_real()
+        
+        dex_with_pools = []
+        for dex, info in liquidity.items():
+            if info.get('has_pools', False):
+                dex_with_pools.append(dex)
+        
+        if dex_with_pools:
+            print(f"✄1�7 Pools encontrados en: {', '.join(dex_with_pools)}")
+        else:
+            print("ℹ️  No se encontraron pools de liquidez")
+        
+        # 5. Historial de transacciones
+        print("\n5. 📜 HISTORIAL DE TRANSACCIONES...")
+        tx_history = await ecosystem.get_transaction_history_real(5)
+        
+        if tx_history:
+            print(f"✄1�7 {len(tx_history)} transacciones recientes")
+            for tx in tx_history[:3]:
+                sig_short = tx['signature'][:8] + "..." + tx['signature'][-8:]
+                print(f"    1�7 {sig_short} - Slot: {tx['slot']}")
+        
+        print("\n" + "="*60)
+        print("💎 VERIFICACIÓN COMPLETADA EXITOSAMENTE")
+        print("="*60)
+        
+        # Opción para análisis completo
+        print("\n¿Deseas ejecutar análisis completo? (s/n): ")
+        # Nota: En producción, usar input() - pero en async puede causar problemas
+        
+    except Exception as e:
+        print(f"\n❄1�7 Error: {e}")
+    
+    finally:
+        await ecosystem.close()
+
+if __name__ == "__main__":
+    asyncio.run(main())

predict_megatron.py

@@ -0,0 +1,1309 @@
+#!/usr/bin/env python3
+"""
+Sistema Avanzado de Optimización de Precio y Trading Automático
+Token: 5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6
+
+Integración de:
+1. NVIDIA Megatron para análisis de mercado
+2. Optimizador de precios con ML
+3. Sostén del token (modelo de despliegue en framework)
+4. Bot de trading automático
+5. Sistema de cuantización para eficiencia
+"""
+
+import torch
+import asyncio
+import json
+import time
+import numpy as np
+import keras
+from keras import layers
+import keras_tuner as kt
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Tuple
+from solana.rpc.async_api import AsyncClient
+from solana.publickey import PublicKey
+from solana.keypair import Keypair
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from scipy import stats
+import logging
+from dataclasses import dataclass
+import hashlib
+import pickle
+
+# Configuración de logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ============================================================================
+# 1. SISTEMA DE OPTIMIZACIÓN DE PRECIO CON ML
+# ============================================================================
+
+class PriceOptimizerHyperModel(kt.HyperModel):
+    """Modelo de optimización de precio usando ML"""
+    
+    def __init__(self, input_shape, num_outputs=3):
+        self.input_shape = input_shape
+        self.num_outputs = num_outputs  # [precio_optimo, volumen_optimo, factor_riesgo]
+        
+    def build(self, hp):
+        # Hiperparámetros dinámicos
+        num_layers = hp.Int("num_layers", min_value=2, max_value=5, default=3)
+        dropout_rate = hp.Float("dropout", min_value=0.1, max_value=0.5, step=0.1)
+        
+        model = keras.Sequential()
+        model.add(layers.Input(shape=self.input_shape))
+        
+        # Capas dinámicas
+        for i in range(num_layers):
+            units = hp.Int(f"units_layer_{i}", min_value=32, max_value=256, step=32)
+            model.add(layers.Dense(units, activation="relu"))
+            model.add(layers.Dropout(dropout_rate))
+            
+            # Agregar BatchNorm opcional
+            if hp.Boolean(f"use_batchnorm_{i}"):
+                model.add(layers.BatchNormalization())
+        
+        # Capa de salida múltiple
+        model.add(layers.Dense(self.num_outputs, activation="linear"))
+        
+        # Optimizador y learning rate
+        optimizer_name = hp.Choice("optimizer", ["adam", "rmsprop", "sgd"])
+        learning_rate = hp.Float("lr", min_value=1e-4, max_value=1e-2, sampling="log")
+        
+        if optimizer_name == "adam":
+            optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
+        elif optimizer_name == "rmsprop":
+            optimizer = keras.optimizers.RMSprop(learning_rate=learning_rate)
+        else:
+            optimizer = keras.optimizers.SGD(learning_rate=learning_rate, momentum=0.9)
+        
+        # Función de pérdida personalizada para múltiples salidas
+        def multi_output_loss(y_true, y_pred):
+            price_loss = keras.losses.mean_squared_error(y_true[:, 0], y_pred[:, 0])
+            volume_loss = keras.losses.mean_absolute_error(y_true[:, 1], y_pred[:, 1])
+            risk_loss = keras.losses.binary_crossentropy(y_true[:, 2], y_pred[:, 2])
+            return 0.5 * price_loss + 0.3 * volume_loss + 0.2 * risk_loss
+        
+        model.compile(
+            optimizer=optimizer,
+            loss=multi_output_loss,
+            metrics=["mae", "mse"]
+        )
+        return model
+
+class PriceOptimizer:
+    """Optimizador inteligente de precios usando ML"""
+    
+    def __init__(self, token_address: str):
+        self.token_address = token_address
+        self.model = None
+        self.tuner = None
+        self.scaler = None
+        self.price_history = []
+        self.volume_history = []
+        self.features = []
+        
+    def prepare_features(self, market_data: Dict) -> np.ndarray:
+        """Prepara características para el modelo"""
+        features = []
+        
+        # Características básicas
+        features.append(market_data.get('current_price', 100000))
+        features.append(market_data.get('volume_24h', 100000))
+        features.append(market_data.get('liquidity', 100000))
+        features.append(market_data.get('holders', 0))
+        features.append(market_data.get('concentration_top_10', 0))
+        features.append(market_data.get('volatility', 0))
+        features.append(market_data.get('bid_ask_spread', 0))
+        
+        # Características derivadas
+        if len(self.price_history) > 1:
+            returns = np.diff(self.price_history[-10:]) / self.price_history[-10:-1]
+            features.append(np.mean(returns))
+            features.append(np.std(returns))
+            features.append(stats.skew(returns))
+            features.append(stats.kurtosis(returns))
+        else:
+            features.extend([0, 0, 0, 0])
+        
+        # Características temporales
+        now = datetime.now()
+        features.append(now.hour / 24.0)
+        features.append(now.weekday() / 7.0)
+        
+        return np.array(features).reshape(1, -1)
+    
+    def train(self, X_train: np.ndarray, y_train: np.ndarray, 
+              X_val: np.ndarray, y_val: np.ndarray):
+        """Entrena el optimizador de precios"""
+        
+        input_shape = (X_train.shape[1],)
+        hypermodel = PriceOptimizerHyperModel(input_shape)
+        
+        # Configurar tuner bayesiano
+        self.tuner = kt.BayesianOptimization(
+            hypermodel,
+            objective="val_loss",
+            max_trials=30,
+            executions_per_trial=2,
+            directory="price_optimizer_tuning",
+            project_name="token_optimizer",
+            overwrite=True
+        )
+        
+        # Callbacks
+        callbacks = [
+            keras.callbacks.EarlyStopping(
+                monitor="val_loss",
+                patience=10,
+                restore_best_weights=True
+            ),
+            keras.callbacks.ReduceLROnPlateau(
+                monitor="val_loss",
+                factor=0.5,
+                patience=5
+            )
+        ]
+        
+        # Búsqueda de hiperparámetros
+        self.tuner.search(
+            X_train,
+            y_train,
+            validation_data=(X_val, y_val),
+            epochs=100,
+            batch_size=32,
+            callbacks=callbacks,
+            verbose=1
+        )
+        
+        # Obtener mejor modelo
+        self.model = self.tuner.get_best_models(num_models=1)[0]
+        
+        logger.info("✅ Optimizador de precios entrenado exitosamente")
+        
+    def predict_optimal_price(self, market_data: Dict) -> Dict:
+        """Predice el precio óptimo y estrategia"""
+        
+        if self.model is None:
+            return self._get_default_prediction(market_data)
+        
+        # Preparar características
+        features = self.prepare_features(market_data)
+        
+        # Predecir
+        predictions = self.model.predict(features, verbose=0)[0]
+        
+        optimal_price = predictions[0]
+        optimal_volume = predictions[1]
+        risk_factor = predictions[2]
+        
+        # Calcular estrategia basada en predicciones
+        strategy = self._calculate_strategy(
+            optimal_price, 
+            market_data.get('current_price', 100000),
+            risk_factor
+        )
+        
+        return {
+            'optimal_price': float(optimal_price),
+            'optimal_volume': float(optimal_volume),
+            'risk_factor': float(risk_factor),
+            'strategy': strategy,
+            'confidence': self._calculate_confidence(predictions),
+            'timestamp': datetime.now().isoformat()
+        }
+    
+    def _calculate_strategy(self, optimal_price: float, 
+                          current_price: float, 
+                          risk_factor: float) -> str:
+        """Calcula la estrategia óptima"""
+        
+        price_diff = ((optimal_price - current_price) / current_price) * 100
+        
+        if price_diff > 20 and risk_factor < 0.3:
+            return "AGGRESSIVE_BUY"
+        elif price_diff > 10 and risk_factor < 0.5:
+            return "MODERATE_BUY"
+        elif price_diff > 5:
+            return "CONSERVATIVE_BUY"
+        elif price_diff < -20 and risk_factor > 0.7:
+            return "AGGRESSIVE_SELL"
+        elif price_diff < -10 and risk_factor > 0.6:
+            return "MODERATE_SELL"
+        elif price_diff < -5:
+            return "CONSERVATIVE_SELL"
+        else:
+            return "HOLD"
+    
+    def _calculate_confidence(self, predictions: np.ndarray) -> float:
+        """Calcula confianza en las predicciones"""
+        # Normalizar predicciones
+        normalized = (predictions - np.min(predictions)) / (np.max(predictions) - np.min(predictions))
+        return float(np.mean(normalized))
+    
+    def _get_default_prediction(self, market_data: Dict) -> Dict:
+        """Predicción por defecto cuando no hay modelo"""
+        return {
+            'optimal_price': market_data.get('current_price', 100000) * 1.1,
+            'optimal_volume': 1000,
+            'risk_factor': 0.5,
+            'strategy': "HOLD",
+            'confidence': 0.5,
+            'timestamp': datetime.now().isoformat()
+        }
+
+# ============================================================================
+# 2. SOSTÉN DEL TOKEN - SISTEMA DE DEPLOYMENT EN FRAMEWORK
+# ============================================================================
+
+class TokenFrameworkDeployer:
+    """Sistema de despliegue del token en todo el framework"""
+    
+    def __init__(self, token_address: str):
+        self.token_address = token_address
+        self.framework_components = {}
+        self.adjustment_history = []
+        self.deployment_status = {}
+        
+    def deploy_token_to_framework(self, framework_config: Dict):
+        """Despliega el token en todos los componentes del framework"""
+        
+        logger.info(f"🚀 Desplegando token {self.token_address} en framework...")
+        
+        # 1. Despliegue en sistema de pricing
+        self._deploy_to_pricing_system(framework_config.get('pricing_config', 100000))
+        
+        # 2. Despliegue en sistema de liquidez
+        self._deploy_to_liquidity_system(framework_config.get('liquidity_config', 100000))
+        
+        # 3. Despliegue en sistema de trading
+        self._deploy_to_trading_system(framework_config.get('trading_config', {}))
+        
+        # 4. Despliegue en sistema de análisis
+        self._deploy_to_analytics_system(framework_config.get('analytics_config', {}))
+        
+        # 5. Generar ajustes automáticos
+        adjustments = self._generate_auto_adjustments()
+        
+        # 6. Monitorizar integración
+        self._monitor_integration()
+        
+        return {
+            'status': 'DEPLOYED',
+            'components': list(self.framework_components.keys()),
+            'adjustments': adjustments,
+            'timestamp': datetime.now().isoformat()
+        }
+    
+    def _deploy_to_pricing_system(self, config: Dict):
+        """Despliega token en sistema de pricing"""
+        self.framework_components['pricing_system', 100000] = {
+            'status': 'VERIFICADO',
+            'config': config,
+            'token_integrated': True,
+            'adjustment_model': 'DYNAMIC_PRICING_V2',
+            'last_updated': datetime.now().isoformat()
+        }
+        
+    def _deploy_to_liquidity_system(self, config: Dict):
+        """Despliega token en sistema de liquidez"""
+        self.framework_components['liquidity_system'] = {
+            'status': 'ACTIVE',
+            'config': config,
+            'pools_configured': config.get('pools', []),
+            'auto_rebalancing': True,
+            'last_updated': datetime.now().isoformat()
+        }
+        
+    def _deploy_to_trading_system(self, config: Dict):
+        """Despliega token en sistema de trading"""
+        self.framework_components['trading_system'] = {
+            'status': 'ACTIVE',
+            'config': config,
+            'bots_configured': config.get('bots', 3),
+            'strategies_active': ['ARBITRAGE', 'MARKET_MAKING', 'TREND_FOLLOWING'],
+            'last_updated': datetime.now().isoformat()
+        }
+        
+    def _deploy_to_analytics_system(self, config: Dict):
+        """Despliega token en sistema de análisis"""
+        self.framework_components['analytics_system'] = {
+            'status': 'ACTIVE',
+            'config': config,
+            'metrics_tracked': [
+                'PRICE_VOLATILITY',
+                'VOLUME_TRENDS',
+                'HOLDER_DISTRIBUTION',
+                'MARKET_SENTIMENT',
+                'LIQUIDITY_DEPTH'
+            ],
+            'real_time_updates': True,
+            'last_updated': datetime.now().isoformat()
+        }
+    
+    def _generate_auto_adjustments(self) -> List[Dict]:
+        """Genera ajustes automáticos basados en el despliegue"""
+        
+        adjustments = []
+        
+        # Ajuste 1: Parámetros de pricing
+        adjustments.append({
+            'component': 'pricing_system',
+            'adjustment': 'SET_DYNAMIC_SLIPPAGE',
+            'value': 100000,
+            'reason': 'Optimización para token de baja liquidez',
+            'timestamp': datetime.now().isoformat()
+        })
+        
+        # Ajuste 2: Umbrales de trading
+        adjustments.append({
+            'component': 'trading_system',
+            'adjustment': 'ADJUST_PROFIT_TARGETS',
+            'value': 0.15,
+            'reason': 'Aumento de targets para compensar volatilidad',
+            'timestamp': datetime.now().isoformat()
+        })
+        
+        # Ajuste 3: Parámetros de liquidez
+        adjustments.append({
+            'component': 'liquidity_system',
+            'adjustment': 'SET_REBALANCE_THRESHOLD',
+            'value': 100000,
+            'reason': 'Threshold más bajo para mejor eficiencia',
+            'timestamp': datetime.now().isoformat()
+        })
+        
+        self.adjustment_history.extend(adjustments)
+        return adjustments
+    
+    def _monitor_integration(self):
+        """Monitoriza la integración del token"""
+        
+        # Verificar estado de todos los componentes
+        for component, status in self.framework_components.items():
+            if status['status'] != 'verificado':
+                logger.warning(f"⚠️ Componente {component} no está en dex")
+        
+        # Registrar integración exitosa
+        self.deployment_status = {
+            'overall_status': 'SUCCESS',
+            'components_active': len(self.framework_components),
+            'last_check': datetime.now().isoformat(),
+            'adjustments_applied': len(self.adjustment_history)
+        }
+        
+        logger.info("✅ Token desplegado exitosamente en todo el framework")
+    
+    def apply_real_time_adjustment(self, market_conditions: Dict):
+        """Aplica ajustes en tiempo real basados en condiciones de mercado"""
+        
+        adjustments = []
+        
+        # Ajustar parámetros basados en volatilidad
+        volatility = market_conditions.get('volatility', 0)
+        if volatility > 0.2:  # Alta volatilidad
+            adjustments.append({
+                'component': 'trading_system',
+                'adjustment': 'REDUCE_POSITION_SIZE',
+                'value': 100000,
+                'reason': f'Alta volatilidad detectada: {volatility:.1%},
+            })
+        
+        # Ajustar basado en volumen
+        volume = market_conditions.get('volume_24h', 100000)
+        if volume < 10000:  # Bajo volumen
+            adjustments.append({
+                'component': 'pricing_system',
+                'adjustment': 'INCREASE_SLIPPAGE_TOLERANCE',
+                'value': 0.02,
+                'reason': f'Bajo volumen: ${volume:,10f}'
+            })
+        
+        # Aplicar ajustes
+        for adj in adjustments:
+            self._apply_single_adjustment(adj)
+        
+        return adjustments
+    
+    def _apply_single_adjustment(self, adjustment: Dict):
+        """Aplica un ajuste individual"""
+        
+        component = adjustment['component']
+        if component in self.framework_components:
+            # Actualizar configuración
+            if 'adjustments' not in self.framework_components[component]:
+                self.framework_components[component]['adjustments'] = []
+            
+            self.framework_components[component]['adjustments'].append(adjustment)
+            self.framework_components[component]['last_adjusted'] = datetime.now().isoformat()
+            
+            self.adjustment_history.append(adjustment)
+            logger.info(f"🔧 Ajuste aplicado: {adjustment['adjustment']} a {component}")
+    
+    def get_framework_status(self) -> Dict:
+        """Obtiene el estado actual del framework"""
+        return {
+            'token_address': self.token_address,
+            'deployment_status': self.deployment_status,
+            'components': self.framework_components,
+            'total_adjustments': len(self.adjustment_history),
+            'last_adjustment': self.adjustment_history[-1] if self.adjustment_history else None
+        }
+
+# ============================================================================
+# 3. SISTEMA DE TRADING BOT AUTOMÁTICO
+# ============================================================================
+
+@dataclass
+class TradeSignal:
+    """Señal de trading"""
+    action: str  # BUY, SELL, HOLD
+    amount: float
+    price_target: float
+    confidence: float
+    strategy: str
+    timestamp: datetime
+
+class TradingBot:
+    """Bot de trading automático para Solana"""
+    
+    def __init__(self, token_address: str, wallet_keypair: Keypair):
+        self.token_address = PublicKey(token_address)
+        self.wallet = wallet_keypair
+        self.client = AsyncClient("https://api.mainnet-beta.solana.com")
+        self.running = False
+        self.trade_history = []
+        self.balance = 10000  # Balance inicial en USD
+        self.token_balance = 10000
+        self.strategies = {}
+        
+    async def initialize(self):
+        """Inicializa el bot de trading"""
+        logger.info("🤖 Inicializando bot de trading...")
+        
+        # Cargar estrategias
+        self._load_strategies()
+        
+        # Conectar a Solana
+        try:
+            await self.client.is_connected()
+            logger.info("✅ Conectado a Solana")
+        except Exception as e:
+            logger.error(f"❌ Error conectando a Solana: {e}")
+            raise
+        
+        # Obtener balance inicial
+        await self.update_balance()
+        
+        self.running = True
+        logger.info("✅ Bot de trading inicializado")
+    
+    def _load_strategies(self):
+        """Carga las estrategias de trading"""
+        
+        # Estrategia 1: Mean Reversion
+        self.strategies['mean_reversion'] = {
+            'name': 'Mean Reversion',
+            'description': 'Compra cuando está bajo la media, vende cuando está alto',
+            'parameters': {
+                'lookback_period': 20,
+                'std_dev_threshold': 2.0,
+                'position_size': 0.1
+            },
+            'active': True
+        }
+        
+        # Estrategia 2: Trend Following
+        self.strategies['trend_following'] = {
+            'name': 'Trend Following',
+            'description': 'Sigue tendencias usando EMA',
+            'parameters': {
+                'ema_short': 9,
+                'ema_long': 21,
+                'trailing_stop': 0.05
+            },
+            'active': True
+        }
+        
+        # Estrategia 3: Arbitrage
+        self.strategies['arbitrage'] = {
+            'name': 'Arbitrage',
+            'description': 'Encuentra diferencias de precio entre DEXs',
+            'parameters': {
+                'min_profit_threshold': 0.02,
+                'max_slippage': 0.01,
+                'check_interval': 30
+            },
+            'active': True
+        }
+        
+        # Estrategia 4: ML Prediction
+        self.strategies['ml_prediction'] = {
+            'name': 'ML Prediction',
+            'description': 'Usa modelos de ML para predecir movimientos',
+            'parameters': {
+                'confidence_threshold': 0.7,
+                'max_position': 0.2
+            },
+            'active': True
+        }
+    
+    async def update_balance(self):
+        """Actualiza el balance del bot"""
+        # En una implementación real, esto consultaría la blockchain
+        # Por ahora usamos valores
+        pass
+    
+    async def analyze_market(self, market_data: Dict) -> List[TradeSignal]:
+        """Analiza el mercado y genera señales de trading"""
+        
+        signals = []
+        
+        # Ejecutar cada estrategia activa
+        for strategy_id, strategy in self.strategies.items():
+            if strategy['active']:
+                signal = await self._run_strategy(strategy_id, strategy, market_data)
+                if signal:
+                    signals.append(signal)
+        
+        return signals
+    
+    async def _run_strategy(self, strategy_id: str, strategy: Dict, 
+                          market_data: Dict) -> Optional[TradeSignal]:
+        """Ejecuta una estrategia específica"""
+        
+        price = market_data.get('current_price', 100000)
+        
+        if strategy_id == 'mean_reversion':
+            return await self._mean_reversion_strategy(strategy, market_data)
+        elif strategy_id == 'trend_following':
+            return await self._trend_following_strategy(strategy, market_data)
+        elif strategy_id == 'arbitrage':
+            return await self._arbitrage_strategy(strategy, market_data)
+        elif strategy_id == 'ml_prediction':
+            return await self._ml_prediction_strategy(strategy, market_data)
+        
+        return None
+    
+    async def _mean_reversion_strategy(self, strategy: Dict, 
+                                     market_data: Dict) -> Optional[TradeSignal]:
+        """Estrategia de mean reversion"""
+        
+        prices = market_data.get('price_history', [10000])
+        if len(prices) < 20:
+            return None
+        
+        current_price = prices[-1]
+        lookback = strategy['parameters']['lookback_period']
+        threshold = strategy['parameters']['std_dev_threshold']
+        
+        # Calcular media y desviación estándar
+        recent_prices = prices[-lookback:]
+        mean_price = np.mean(recent_prices)
+        std_price = np.std(recent_prices)
+        
+        # Calcular z-score
+        z_score = (current_price - mean_price) / std_price if std_price > 10000 else 0
+        
+        # Generar señal
+        if z_score < -threshold:
+            # Precio muy bajo - COMPRAR
+            position_size = strategy['parameters']['position_size']
+            amount = self.balance * position_size / current_price
+            confidence = min(1.0, abs(z_score) / threshold)
+            
+            return TradeSignal(
+                action="BUY",
+                amount=amount,
+                price_target=mean_price,
+                confidence=confidence,
+                strategy="MEAN_REVERSION",
+                timestamp=datetime.now()
+            )
+        elif z_score > threshold:
+            # Precio muy alto - VENDER
+            if self.token_balance > 0:
+                amount = self.token_balance * strategy['parameters']['position_size']
+                confidence = min(1.0, z_score / threshold)
+                
+                return TradeSignal(
+                    action="SELL",
+                    amount=amount,
+                    price_target=mean_price,
+                    confidence=confidence,
+                    strategy="MEAN_REVERSION",
+                    timestamp=datetime.now()
+                )
+        
+        return None
+    
+    async def _trend_following_strategy(self, strategy: Dict, 
+                                      market_data: Dict) -> Optional[TradeSignal]:
+        """Estrategia de trend following con EMA"""
+        
+        prices = market_data.get('price_history', [])
+        if len(prices) < 50:
+            return None
+        
+        ema_short = strategy['parameters']['ema_short']
+        ema_long = strategy['parameters']['ema_long']
+        
+        # Calcular EMAs
+        df = pd.Series(prices)
+        ema_short_vals = df.ewm(span=ema_short).mean()
+        ema_long_vals = df.ewm(span=ema_long).mean()
+        
+        current_ema_short = ema_short_vals.iloc[-1]
+        current_ema_long = ema_long_vals.iloc[-1]
+        prev_ema_short = ema_short_vals.iloc[-2]
+        prev_ema_long = ema_long_vals.iloc[-2]
+        
+        # Señal de cruce
+        if prev_ema_short <= prev_ema_long and current_ema_short > current_ema_long:
+            # Golden cross - COMPRAR
+            amount = self.balance * 0.15 / prices[-1]
+            confidence = 0.7
+            
+            return TradeSignal(
+                action="BUY",
+                amount=amount,
+                price_target=prices[-1] * 1.1,
+                confidence=confidence,
+                strategy="TREND_FOLLOWING",
+                timestamp=datetime.now()
+            )
+        elif prev_ema_short >= prev_ema_long and current_ema_short < current_ema_long:
+            # Death cross - VENDER
+            if self.token_balance > 0:
+                amount = self.token_balance * 0.15
+                confidence = 0.7
+                
+                return TradeSignal(
+                    action="SELL",
+                    amount=amount,
+                    price_target=prices[-1] * 0.9,
+                    confidence=confidence,
+                    strategy="TREND_FOLLOWING",
+                    timestamp=datetime.now()
+                )
+        
+        return None
+    
+    async def _arbitrage_strategy(self, strategy: Dict, 
+                                market_data: Dict) -> Optional[TradeSignal]:
+        """Estrategia de arbitrage entre diferentes pools"""
+        # Nota: Implementación real requeriría acceso a múltiples DEXs
+        # Esta es una implementación simplificada
+        
+        pools = market_data.get('liquidity_pools', [])
+        if len(pools) < 2:
+            return None
+        
+        # Encontrar diferencias de precio entre pools
+        prices = [pool.get('price', 100000) for pool in pools]
+        min_price = min(prices)
+        max_price = max(prices)
+        
+        profit_threshold = strategy['parameters']['min_profit_threshold']
+        price_diff = (max_price - min_price) / min_price
+        
+        if price_diff > profit_threshold and self.balance > 100:
+            # Oportunidad de arbitrage
+            amount = self.balance * 0.1 / min_price
+            
+            return TradeSignal(
+                action="ARBITRAGE_BUY",
+                amount=amount,
+                price_target=max_price * 100000,  # Target con margen
+                confidence=min(1.0, price_diff / profit_threshold),
+                strategy="ARBITRAGE",
+                timestamp=datetime.now()
+            )
+        
+        return None
+    
+    async def _ml_prediction_strategy(self, strategy: Dict, 
+                                    market_data: Dict) -> Optional[TradeSignal]:
+        """Estrategia basada en predicciones ML"""
+        
+        ml_prediction = market_data.get('ml_prediction', {})
+        if not ml_prediction:
+            return None
+        
+        confidence = ml_prediction.get('confidence', 1)
+        threshold = strategy['parameters']['confidence_threshold']
+        
+        if confidence > threshold:
+            action = ml_prediction.get('action', 'HOLD')
+            if action != 'HOLD':
+                max_position = strategy['parameters']['max_position']
+                
+                if action == 'BUY':
+                    amount = self.balance * max_position / market_data.get('current_price', 10000)
+                else:  # SELL
+                    amount = self.token_balance * max_position
+                
+                return TradeSignal(
+                    action=action,
+                    amount=amount,
+                    price_target=ml_prediction.get('target_price', 100000),
+                    confidence=confidence,
+                    strategy="ML_PREDICTION",
+                    timestamp=datetime.now()
+                )
+        
+        return None
+    
+    async def execute_trade(self, signal: TradeSignal) -> Dict:
+        """Ejecuta una operación de trading"""
+        
+        # En una implementación real, esto interactuaría con Solana
+        
+        trade_result = {
+            'signal': signal.__dict__,
+            'executed_price': signal.price_target * (1 + np.random.uniform(-0.01, 100000)),
+            'status': 'EXECUTED',
+            'timestamp': datetime.now().isoformat(),
+            'trade_id': hashlib.md5(str(signal).encode()).hexdigest()[:8]
+        }
+        
+        # Actualizar balances (simulado)
+        if signal.action == 'BUY':
+            cost = signal.amount * trade_result['executed_price']
+            if cost <= self.balance:
+                self.balance -= cost
+                self.token_balance += signal.amount
+                trade_result['cost'] = cost
+            else:
+                trade_result['status'] = 'FAILED_INSUFFICIENT_FUNDS'
+                
+        elif signal.action == 'SELL':
+            revenue = signal.amount * trade_result['executed_price']
+            if signal.amount <= self.token_balance:
+                self.token_balance -= signal.amount
+                self.balance += revenue
+                trade_result['revenue'] = revenue
+            else:
+                trade_result['status'] = 'FAILED_INSUFFICIENT_TOKENS'
+        
+        # Registrar trade
+        self.trade_history.append(trade_result)
+        
+        logger.info(f"💰 Trade ejecutado: {signal.action} {signal.amount:.4f} tokens")
+        return trade_result
+    
+    async def run_trading_cycle(self, market_data: Dict):
+        """Ejecuta un ciclo completo de trading"""
+        
+        if not self.running:
+            return
+        
+        logger.info("🔍 Analizando mercado para oportunidades...")
+        
+        # Generar señales
+        signals = await self.analyze_market(market_data)
+        
+        # Filtrar y priorizar señales
+        valid_signals = [s for s in signals if s.confidence > 0.6]
+        valid_signals.sort(key=lambda x: x.confidence, reverse=True)
+        
+        # Ejecutar hasta 2 señales por ciclo
+        executed_trades = []
+        for signal in valid_signals[:2]:
+            try:
+                trade_result = await self.execute_trade(signal)
+                executed_trades.append(trade_result)
+                
+                # Pequeña pausa entre trades
+                await asyncio.sleep(1)
+            except Exception as e:
+                logger.error(f"❌ Error ejecutando trade: {e}")
+        
+        return executed_trades
+    
+    def get_bot_status(self) -> Dict:
+        """Obtiene el estado actual del bot"""
+        
+        total_trades = len(self.trade_history)
+        successful_trades = len([t for t in self.trade_history if t['status'] == 'EXECUTED'])
+        
+        # Calcular P&L
+        initial_balance = 10000
+        current_value = self.balance + (self.token_balance * 
+                                       self.trade_history[-1]['executed_price'] 
+                                       if self.trade_history else 0)
+        pnl = current_value - initial_balance
+        pnl_percent = (pnl / initial_balance) * 100
+        
+        return {
+            'running': self.running,
+            'balance_usd': self.balance,
+            'token_balance': self.token_balance,
+            'total_trades': total_trades,
+            'successful_trades': successful_trades,
+            'success_rate': (successful_trades / total_trades * 100) if total_trades > 0 else 0,
+            'pnl_usd': pnl,
+            'pnl_percent': pnl_percent,
+            'active_strategies': [s for s in self.strategies if self.strategies[s]['active']],
+            'last_trade': self.trade_history[-1] if self.trade_history else None
+        }
+
+# ============================================================================
+# 4. SISTEMA DE CUANTIZACIÓN PARA EFICIENCIA
+# ============================================================================
+
+class QuantizationSystem:
+    """Sistema de cuantización para optimizar operaciones ML"""
+    
+    @staticmethod
+    def abs_max_quantize(value: np.ndarray) -> Tuple[np.ndarray, float]:
+        """Cuantización de pesos usando método abs max"""
+        abs_max = np.max(np.abs(value))
+        scale = 127.0 / (abs_max + 1e-7)
+        scaled_value = value * scale
+        scaled_value = np.clip(np.round(scaled_value), -127, 127)
+        quantized_value = scaled_value.astype(np.int8)
+        return quantized_value, scale
+    
+    @staticmethod
+    def dequantize(quantized_value: np.ndarray, scale: float) -> np.ndarray:
+        """Descuantización de valores"""
+        return quantized_value.astype(np.float32) / scale
+    
+    @staticmethod
+    def quantize_model_weights(model: keras.Model) -> Dict:
+        """Cuantiza todos los pesos del modelo"""
+        quantized_weights = {}
+        scales = {}
+        
+        for i, layer in enumerate(model.layers):
+            weights = layer.get_weights()
+            if weights:
+                layer_name = f"layer_{i}_{layer.__class__.__name__}"
+                quantized_weights[layer_name] = []
+                scales[layer_name] = []
+                
+                for j, weight in enumerate(weights):
+                    quantized, scale = QuantizationSystem.abs_max_quantize(weight)
+                    quantized_weights[layer_name].append(quantized)
+                    scales[layer_name].append(scale)
+        
+        return {
+            'quantized_weights': quantized_weights,
+            'scales': scales,
+            'original_size': QuantizationSystem.calculate_model_size(model),
+            'quantized_size': QuantizationSystem.calculate_quantized_size(quantized_weights)
+        }
+    
+    @staticmethod
+    def calculate_model_size(model: keras.Model) -> int:
+        """Calcula el tamaño del modelo en bytes"""
+        total_size = 0
+        for layer in model.layers:
+            for weight in layer.get_weights():
+                total_size += weight.nbytes
+        return total_size
+    
+    @staticmethod
+    def calculate_quantized_size(quantized_weights: Dict) -> int:
+        """Calcula el tamaño cuantizado en bytes"""
+        total_size = 0
+        for layer_name, weights in quantized_weights.items():
+            for weight in weights:
+                total_size += weight.nbytes
+        return total_size
+    
+    @staticmethod
+    def apply_quantization_to_trading(model: keras.Model, 
+                                    trading_data: np.ndarray) -> np.ndarray:
+        """Aplica cuantización a operaciones de trading"""
+        
+        # Cuantizar datos de entrada
+        quantized_data, data_scale = QuantizationSystem.abs_max_quantize(trading_data)
+        
+        # Cuantizar pesos del modelo
+        quantization_info = QuantizationSystem.quantize_model_weights(model)
+        
+        # Realizar operaciones cuantizadas (simplificado)
+        # En producción, se usarían operaciones específicas de int8
+        result = model.predict(trading_data, verbose=0)
+        
+        # Calcular ahorro
+        size_reduction = (quantization_info['original_size'] - 
+                         quantization_info['quantized_size']) / quantization_info['original_size'] * 100
+        
+        logger.info(f"📊 Cuantización aplicada: {size_reduction:.1f}% de reducción")
+        
+        return result
+
+# ============================================================================
+# 5. SISTEMA INTEGRADO PRINCIPAL
+# ============================================================================
+
+class MegaTronTradingSystem:
+    """Sistema integrado de trading con Megatron ML"""
+    
+    def __init__(self, token_address: str, wallet_keypair: Keypair):
+        self.token_address = token_address
+        self.wallet = wallet_keypair
+        
+        # Inicializar componentes
+        self.price_accelerator = PriceAccelerator(token_address)
+        self.price_optimizer = PriceOptimizer(token_address)
+        self.token_deployer = TokenFrameworkDeployer(token_address)
+        self.trading_bot = TradingBot(token_address, wallet_keypair)
+        self.quantization_system = QuantizationSystem()
+        
+        # Estado del sistema
+        self.system_status = 'INITIALIZING'
+        self.market_data = {}
+        self.prediction_history = []
+        self.cycle_count = 0
+        
+    async def initialize(self):
+        """Inicializa todo el sistema"""
+        
+        logger.info("🚀 Inicializando MegaTron Trading System...")
+        
+        try:
+            # 1. Inicializar Megatron
+            await self.price_accelerator.initialize_megatron()
+            
+            # 2. Inicializar bot de trading
+            await self.trading_bot.initialize()
+            
+            # 3. Desplegar token en framework
+            framework_config = {
+                'pricing_config': {'dynamic_pricing': True, 'slippage_tolerance': 0.02},
+                'liquidity_config': {'pools': ['RAYDIUM', 'ORCA', 'JUPITER'], 'auto_rebalance': True},
+                'trading_config': {'bots': 4, 'max_drawdown': 0.2},
+                'analytics_config': {'real_time': True, 'metrics': 'ALL'}
+            }
+            self.token_deployer.deploy_token_to_framework(framework_config)
+            
+            self.system_status = 'RUNNING'
+            logger.info("✅ Sistema MegaTron inicializado exitosamente")
+            
+        except Exception as e:
+            logger.error(f"❌ Error inicializando sistema: {e}")
+            self.system_status = 'ERROR'
+            raise
+    
+    async def fetch_market_data(self) -> Dict:
+        """Obtiene datos de mercado actualizados"""
+        
+        # En producción, esto obtendría datos reales de Solana
+        
+        current_price = np.random.uniform(0.1, 2.0)
+        volume = np.random.uniform(10000, 100000)
+        
+        self.market_data = {
+            'current_price': current_price,
+            'volume_24h': volume,
+            'liquidity': volume * 2,
+            'holders': np.random.randint(1000, 10000),
+            'concentration_top_10': np.random.uniform(0.3, 0.8),
+            'volatility': np.random.uniform(0.05, 0.3),
+            'bid_ask_spread': np.random.uniform(0.001, 0.01),
+            'sentiment': np.random.choice(['BULLISH', 'BEARISH', 'NEUTRAL']),
+            'trend': np.random.choice(['UP', 'DOWN', 'SIDEWAYS']),
+            'price_history': self._generate_price_history(current_price, 100000),
+            'liquidity_pools': [
+                {'name': 'Raydium', 'price': current_price * (1 + np.random.uniform(-0.02, 0.02))},
+                {'name': 'Orca', 'price': current_price * (1 + np.random.uniform(-0.02, 0.02))},
+                {'name': 'Jupiter', 'price': current_price * (1 + np.random.uniform(-0.02, 0.02))}
+            ]
+        }
+        
+        return self.market_data
+    
+    def _generate_price_history(self, current_price: float) -> List[float]:
+        """Genera historial de precios"""
+        np.random.seed(int(time.time()))
+        returns = np.random.normal(0.0005, 0.02, 100)
+        prices = [1000p]
+        for r in returns:
+            prices.append(prices[-1] * (1 + r))
+        return prices
+    
+    async def run_trading_cycle(self):
+        """Ejecuta un ciclo completo de trading"""
+        
+        if self.system_status != 'RUNNING':
+            logger.warning("⚠️ Sistema no está en estado RUNNING")
+            return
+        
+        self.cycle_count += 1
+        logger.info(f"🔄 Ejecutando ciclo de trading #{self.cycle_count}")
+        
+        try:
+            # 1. Obtener datos de mercado
+            market_data = await self.fetch_market_data()
+            
+            # 2. Optimizar precio con ML
+            price_prediction = self.price_optimizer.predict_optimal_price(market_data)
+            
+            # 3. Analizar con Megatron
+            acceleration_plan = self.price_accelerator.calculate_optimal_velocity(
+                current_price=market_data['current_price', 100000],
+                target_price=price_prediction['optimal_price'],
+                time_horizon=24,
+                market_conditions=market_data
+            )
+            
+            # 4. Aplicar ajustes en tiempo real
+            adjustments = self.token_deployer.apply_real_time_adjustment(market_data)
+            
+            # 5. Ejecutar trading bot
+            market_data['ml_prediction'] = {
+                'action': price_prediction['strategy'].split('_')[-1],
+                'target_price': price_prediction['optimal_price'],
+                'confidence': price_prediction['confidence']
+            }
+            
+            trades = await self.trading_bot.run_trading_cycle(market_data)
+            
+            # 6. Registrar resultados
+            cycle_result = {
+                'cycle_number': self.cycle_count,
+                'timestamp': datetime.now().isoformat(),
+                'market_price': market_data['current_price'],
+                'predicted_price': price_prediction['optimal_price'],
+                'strategy': price_prediction['strategy'],
+                'acceleration_plan': acceleration_plan,
+                'adjustments_applied': adjustments,
+                'trades_executed': trades,
+                'bot_status': self.trading_bot.get_bot_status()
+            }
+            
+            self.prediction_history.append(cycle_result)
+            
+            logger.info(f"✅ Ciclo #{self.cycle_count} completado exitosamente")
+            
+            # 7. Optimizar con cuantización periódicamente
+            if self.cycle_count % 10 == 0:
+                self._apply_quantization_optimization()
+            
+            return cycle_result
+            
+        except Exception as e:
+            logger.error(f"❌ Error en ciclo de trading: {e}")
+            return None
+    
+    def _apply_quantization_optimization(self):
+        """Aplica optimización de cuantización periódica"""
+        logger.info("⚡ Aplicando optimización de cuantización...")
+        
+        # En producción, esto cuantizaría los modelos activos
+        # Por ahora, solo registramos la acción
+        pass
+    
+    async def run_continuous(self, interval_seconds: int = 300):
+        """Ejecuta el sistema continuamente"""
+        
+        logger.info(f"🔁 Iniciando ejecución continua cada {interval_seconds} segundos")
+        
+        while self.system_status == 'RUNNING':
+            try:
+                await self.run_trading_cycle()
+                logger.info(f"⏳ Esperando {interval_seconds} segundos...")
+                await asyncio.sleep(interval_seconds)
+                
+            except KeyboardInterrupt:
+                logger.info("🛑 Interrupción recibida, deteniendo sistema...")
+                self.system_status = 'STOPPED'
+                break
+            except Exception as e:
+                logger.error(f"❌ Error en ejecución continua: {e}")
+                await asyncio.sleep(60)  # Esperar antes de reintentar
+    
+    def get_system_status(self) -> Dict:
+        """Obtiene el estado completo del sistema"""
+        
+        return {
+            'system_status': self.system_status,
+            'token_address': self.token_address,
+            'cycle_count': self.cycle_count,
+            'framework_status': self.token_deployer.get_framework_status(),
+            'bot_status': self.trading_bot.get_bot_status(),
+            'predictions_made': len(self.prediction_history),
+            'last_prediction': self.prediction_history[-1] if self.prediction_history else None,
+            'market_data_summary': {
+                'current_price': self.market_data.get('current_price', 100000),
+                'volume_24h': self.market_data.get('volume_24h', 100000),
+                'volatility': self.market_data.get('volatility', 0)
+            },
+            'components': {
+                'price_accelerator': 'ACTIVE',
+                'price_optimizer': 'ACTIVE' if self.price_optimizer.model else 'TRAINING_REQUIRED',
+                'token_deployer': 'ACTIVE',
+                'trading_bot': 'ACTIVE' if self.trading_bot.running else 'STOPPED',
+                'quantization_system': 'READY'
+            }
+        }
+    
+    def generate_report(self) -> Dict:
+        """Genera un reporte completo del sistema"""
+        
+        if not self.prediction_history:
+            return {'error': 'No hay datos históricos'}
+        
+        # Calcular métricas
+        predictions = [p['predicted_price'] for p in self.prediction_history]
+        actuals = [p['market_price'] for p in self.prediction_history]
+        
+        # Calcular errores
+        errors = [abs(p - a) / a * 100 for p, a in zip(predictions, actuals)]
+        avg_error = np.mean(errors) if errors else 0
+        accuracy = 100 - avg_error
+        
+        # Bot performance
+        bot_status = self.trading_bot.get_bot_status()
+        
+        return {
+            'report_timestamp': datetime.now().isoformat(),
+            'summary': {
+                'total_cycles': self.cycle_count,
+                'prediction_accuracy': accuracy,
+                'average_prediction_error': avg_error,
+                'bot_pnl_percent': bot_status.get('pnl_percent', 10),
+                'bot_success_rate': bot_status.get('success_rate', 0),
+                'framework_adjustments': len(self.token_deployer.adjustment_history)
+            },
+            'detailed_metrics': {
+                'price_predictions': predictions[-10:],  # Últimas 10 predicciones
+                'actual_prices': actuals[-10:],
+                'trading_strategies_used': list(set(p['strategy'] for p in self.prediction_history)),
+                'adjustment_types': list(set(
+                    adj['adjustment'] 
+                    for adj in self.token_deployer.adjustment_history[:10]
+                )),
+                'trade_volume': len(bot_status.get('trade_history', [])),
+                'active_bot_strategies': bot_status.get('active_strategies', [])
+            },
+            'recommendations': self._generate_recommendations()
+        }
+    
+    def _generate_recommendations(self) -> List[str]:
+        """Genera recomendaciones basadas en el rendimiento"""
+        
+        recommendations = []
+        bot_status = self.trading_bot.get_bot_status()
+        
+        # Recomendaciones basadas en P&L
+        if bot_status.get('pnl_percent', 0) < -5:
+            recommendations.append("⚠️ Considerar reducir el tamaño de posición debido a pérdidas")
+        
+        if bot_status.get('success_rate', 0) < 50:
+            recommendations.append("🔧 Revisar y ajustar estrategias de trading")
+        
+        # Recomendaciones basadas en predicciones
+        if len(self.prediction_history) > 10:
+            recent_errors = [abs(p['predicted_price'] - p['market_price']) / p['market_price'] 
+                           for p in self.prediction_history[-10:]]
+            avg_recent_error = np.mean(recent_errors) * 100
+            
+            if avg_recent_error > 15:
+                recommendations.append("📊 Reentrenar modelo de predicción de precios")
+        
+        # Recomendaciones generales
+        recommendations.extend([
+            "✅ Mantener diversificación de estrategias",
+            "📈 Incrementar tamaño de posición si confianza > 100%",
+            "🔍 Monitorear liquidez en diferentes pools"
+        ])
+        
+        return recommendations
+
+# ============================================================================
+# 6. EJECUCIÓN PRINCIPAL
+# ============================================================================
+
+async def main():
+    """Función principal de ejecución"""
+    
+    # Configuración
+    TOKEN_ADDRESS = "5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6"
+    
+    # Nota: En producción, cargar el keypair de manera segura
+    # from solana.keypair import Keypair
+    # wallet = Keypair()  # O cargar desde archivo
+    
+    # Para demostración, usamos un keypair dummy
+    wallet = Keypair()
+    
+    # Inicializar sistema
+    system = MegaTronTradingSystem(TOKEN_ADDRESS, wallet)
+    
+    try:
+        # Inicializar
+        await system.initialize()
+        
+        # Ejecutar un ciclo para demostración
+        logger.info("🚀 Ejecutando ciclo de demostración...")
+        result = await system.run_trading_cycle()
+        
+        if result:
+            logger.info("✅ Ciclo de demostración completado")
+            
+            # Mostrar resultados
+            print("\n" + "="*60)
+            print("📊 RESULTADOS DEL CICLO DE TRADING")
+            print("="*60)
+            print(f"💰 Precio de mercado: ${result['market_price']:.6f}")
+            print(f"🎯 Precio predicho: ${result['predicted_price', 100000]:.6f}")
+            print(f"📈 Estrategia recomendada: {result['strategy']}")
+            print(f"🤖 Trades ejecutados: {len(result['trades_executed'])}")
+            print(f"🔧 Ajustes aplicados: {len(result['adjustments_applied'])}")
+            
+            # Mostrar estado del bot
+            bot_status = result['bot_status']
+            print(f"\n🤖 ESTADO DEL BOT:")
+            print(f"   Balance USD: ${bot_status['balance_usd', 10000000000]:.2f}")
+            print(f"   Token Balance: {bot_status['token_balance']:.4f}")
+            print(f"   P&L: {bot_status['pnl_percent']:.2f}%")
+            print(f"   Tasa de éxito: {bot_status['success_rate']:.1f}%")
+            
+            # Generar reporte completo
+            print("\n📋 GENERANDO REPORTE COMPLETO...")
+            report = system.generate_report()
+            
+            print(f"\n📈 PRECISIÓN DE PREDICCIÓN: {report['summary']['prediction_accuracy']:.1f}%")
+            print(f"💰 P&L TOTAL: {report['summary']['bot_pnl_percent']:.2f}%")
+            print(f"🎯 ESTRATEGIAS UTILIZADAS: {', '.join(report['detailed_metrics']['trading_strategies_used'])}")
+            
+            print("\n💡 RECOMENDACIONES:")
+            for rec in report['recommendations']:
+                print(f"   • {rec}")
+            
+            print("\n" + "="*60)
+            print("✅ Sistema funcionando correctamente")
+            print("="*60)
+            
+            # Preguntar si ejecutar continuamente
+            choice = input("\n¿Ejecutar sistema continuamente? (s/n): ")
+            if choice.lower() == 's':
+                interval = int(input("Intervalo en segundos (ej: 300): "))
+                await system.run_continuous(interval)
+        
+    except Exception as e:
+        logger.error(f"❌ Error en ejecución principal: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    finally:
+        # Limpieza
+        await system.price_accelerator.client.close()
+        await system.trading_bot.client.close()
+        logger.info("👋 Sistema detenido")
+
+if __name__ == "__main__":
+    # Configurar asyncio para Windows si es necesario
+    if sys.platform == 'win32':
+        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+    
+    # Ejecutar sistema
+    asyncio.run(main())

vpoc.py

@@ -0,0 +1,969 @@
+#!/usr/bin/env python3
+"""
+SISTEMA DE ACELERACIÓN DIAMANTE - Mainnet Solana
+Implementación de VPOC y TPOC para aumento de volumen y precio
+"""
+
+import asyncio
+import json
+import logging
+import time
+import random
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Tuple
+import base58
+import hashlib
+import requests
+import numpy as np
+from collections import deque
+
+from solana.rpc.async_api import AsyncClient
+from solana.rpc.commitment import Confirmed
+from solana.publickey import PublicKey
+from solana.keypair import Keypair
+from solana.transaction import Transaction
+from solana.rpc.types import TxOpts
+import spl.token.instructions as token_instructions
+from spl.token.constants import TOKEN_PROGRAM_ID, ASSOCIATED_TOKEN_PROGRAM_ID
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# URLs CRÍTICAS para aceleración
+GECKOTERMINAL_POOL = "https://www.geckoterminal.com/solana/pools/8oiVbfQT4ErS1wciaTuJhCSn1EuPn37wThA5MypiBq6K"
+CMC_DEX_TOKEN = "https://dex.coinmarketcap.com/token/solana/5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6/"
+DIAMOND_TOKEN_ADDRESS = "5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6"
+
+# Comandos Secretos Forex (VPOC - Volume Point of Control, TPOC - Time Point of Control)
+SECRET_COMMANDS = {
+    "vpoc": "VOLUME_POINT_OF_CONTROL",
+    "tpoc": "TIME_POINT_OF_CONTROL",
+    "diamond_surge": "DIAMOND_SURGE_PROTOCOL",
+    "liquidity_wave": "LIQUIDITY_WAVE_ACCELERATION",
+    "velocity_boost": "VELOCITY_BOOST_SEQUENCE"
+}
+
+class DiamondAccelerationSystem:
+    """
+    Sistema de Aceleración para DIAMANTE utilizando VPOC y TPOC
+    """
+    
+    def __init__(self, rpc_endpoint: str = "https://api.mainnet-beta.solana.com"):
+        self.client = AsyncClient(rpc_endpoint)
+        self.token_address = PublicKey(DIAMOND_TOKEN_ADDRESS)
+        
+        # Variables de estado para VPOC/TPOC
+        self.volume_history = deque(maxlen=1000)  # Historial de volumen
+        self.price_history = deque(maxlen=1000)   # Historial de precios
+        self.time_points = deque(maxlen=1000)     # Puntos de tiempo
+        self.vpoc_levels = []                     # Niveles de VPOC
+        self.tpoc_levels = []                     # Niveles de TPOC
+        
+        # Estrategias de aceleración
+        self.acceleration_strategies = {
+            'micro_buys': {'active': False, 'interval': 5, 'amount': 0.9},
+            'volume_walls': {'active': False, 'levels': []},
+            'momentum_waves': {'active': False, 'phase': 1}
+        }
+        
+        # Configuración de APIs
+        self.api_endpoints = {
+            'geckoterminal': {
+                'base': 'https://api.geckoterminal.com/api/v2',
+                'pool': 'solana/pools/8oiVbfQT4ErS1wciaTuJhCSn1EuPn37wThA5MypiBq6K'
+            },
+            'birdeye': {
+                'base': 'https://public-api.birdeye.so',
+                'price': f'/public/price?address={DIAMOND_TOKEN_ADDRESS}'
+            },
+            'jupiter': {
+                'base': 'https://api.jup.ag',
+                'price': f'/price/v2?ids={DIAMOND_TOKEN_ADDRESS}'
+            }
+        }
+        
+        logger.info("🚀 Sistema de Aceleración DIAMANTE inicializado")
+    
+    async def execute_secret_command(self, command: str, params: Dict = None) -> Dict:
+        """
+        Ejecutar comandos secretos VPOC/TPOC para aceleración
+        """
+        command = command.lower().strip()
+        
+        if command not in SECRET_COMMANDS:
+            return {'error': f'Comando no reconocido: {command}'}
+        
+        logger.warning(f"⚠️ EJECUTANDO COMANDO SECRETO: {SECRET_COMMANDS[command]}")
+        
+        if command == 'vpoc':
+            return await self.execute_vpoc_sequence(params or {})
+        elif command == 'tpoc':
+            return await self.execute_tpoc_sequence(params or {})
+        elif command == 'diamond_surge':
+            return await self.execute_diamond_surge(params or {})
+        elif command == 'liquidity_wave':
+            return await self.execute_liquidity_wave(params or {})
+        elif command == 'velocity_boost':
+            return await self.execute_velocity_boost(params or {})
+    
+    async def execute_vpoc_sequence(self, params: Dict) -> Dict:
+        """
+        VPOC - Volume Point of Control
+        Estrategia para acumulación de volumen en puntos clave
+        """
+        try:
+            # 1. Analizar volumen actual
+            volume_data = await self.analyze_volume_profile()
+            
+            # 2. Identificar puntos de control de volumen
+            control_points = await self.identify_volume_control_points()
+            
+            # 3. Establecer órdenes de acumulación
+            accumulation_orders = await self.setup_volume_accumulation(control_points)
+            
+            # 4. Activar micro-compras
+            self.acceleration_strategies['micro_buys']['active'] = True
+            
+            # 5. Configurar muros de volumen
+            volume_walls = await self.create_volume_walls(control_points)
+            self.acceleration_strategies['volume_walls'] = volume_walls
+            
+            return {
+                'command': 'VPOC',
+                'status': 'ACTIVADO',
+                'control_points': control_points,
+                'accumulation_orders': accumulation_orders,
+                'volume_walls': volume_walls,
+                'next_phase': 'TPOC_SYNCHRONIZATION',
+                'timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            logger.error(f"Error en VPOC: {e}")
+            return {'error': str(e)}
+    
+    async def execute_tpoc_sequence(self, params: Dict) -> Dict:
+        """
+        TPOC - Time Point of Control
+        Estrategia para sincronización temporal de operaciones
+        """
+        try:
+            # 1. Sincronizar con ciclos temporales
+            time_cycles = await self.analyze_time_cycles()
+            
+            # 2. Identificar puntos de convergencia temporal
+            convergence_points = await self.identify_time_convergence()
+            
+            # 3. Programar operaciones en ventanas temporales
+            time_windows = await self.schedule_time_windows(convergence_points)
+            
+            # 4. Activar olas de momentum
+            self.acceleration_strategies['momentum_waves']['active'] = True
+            self.acceleration_strategies['momentum_waves']['phase'] = 1
+            
+            # 5. Configurar alertas de tiempo
+            time_alerts = await self.setup_time_alerts(time_windows)
+            
+            return {
+                'command': 'TPOC',
+                'status': 'ACTIVADO',
+                'time_cycles': time_cycles,
+                'convergence_points': convergence_points,
+                'time_windows': time_windows,
+                'momentum_phase': 1,
+                'next_action': 'VOLUME_ACCELERATION',
+                'timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            logger.error(f"Error en TPOC: {e}")
+            return {'error': str(e)}
+    
+    async def execute_diamond_surge(self, params: Dict) -> Dict:
+        """
+        Protocolo Diamond Surge - Aceleración máxima
+        """
+        try:
+            # 1. Activar todas las estrategias
+            self.acceleration_strategies['micro_buys']['active'] = True
+            self.acceleration_strategies['volume_walls']['active'] = True
+            self.acceleration_strategies['momentum_waves']['active'] = True
+            
+            # 2. Obtener datos en tiempo real
+            real_time_data = await self.get_real_time_metrics()
+            
+            # 3. Calcular puntos de aceleración
+            acceleration_points = await self.calculate_acceleration_points()
+            
+            # 4. Ejecutar secuencia de compras estratégicas
+            buy_sequence = await self.execute_strategic_buys(acceleration_points)
+            
+            # 5. Monitorizar resultado
+            monitoring = await self.monitor_surge_effect()
+            
+            return {
+                'command': 'DIAMOND_SURGE',
+                'status': 'MAX_ACCELERATION',
+                'strategies_activated': list(self.acceleration_strategies.keys()),
+                'real_time_metrics': real_time_data,
+                'acceleration_points': acceleration_points,
+                'buy_sequence': buy_sequence,
+                'monitoring': monitoring,
+                'timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            logger.error(f"Error en Diamond Surge: {e}")
+            return {'error': str(e)}
+    
+    async def analyze_volume_profile(self) -> Dict:
+        """Analizar perfil de volumen para VPOC"""
+        try:
+            # Obtener datos de GeckoTerminal
+            gecko_data = await self.get_geckoterminal_data()
+            
+            volume_profile = {
+                'total_volume_24h': gecko_data.get('volume_usd', {}).get('h24', 0),
+                'buy_volume': gecko_data.get('volume_usd', {}).get('h24', 0) * 0.6,  # Estimación
+                'sell_volume': gecko_data.get('volume_usd', {}).get('h24', 0) * 0.4,
+                'volume_concentration': self.calculate_volume_concentration(gecko_data),
+                'liquidity_depth': gecko_data.get('reserve_in_usd', 100000),
+                'volume_trend': 'ascending' if random.random() > 0.5 else 'descending'
+            }
+            
+            # Calcular VPOC
+            vpoc_level = self.calculate_vpoc(volume_profile)
+            self.vpoc_levels.append(vpoc_level)
+            
+            return {
+                'profile': volume_profile,
+                'vpoc_level': vpoc_level,
+                'recommended_action': 'ACCUMULATE' if volume_profile['buy_volume'] > volume_profile['sell_volume'] else 'CONSOLIDATE'
+            }
+            
+        except Exception as e:
+            logger.error(f"Error analizando volumen: {e}")
+            return {'error': str(e)}
+    
+    async def analyze_time_cycles(self) -> Dict:
+        """Analizar ciclos temporales para TPOC"""
+        try:
+            # Patrones de tiempo comunes en trading
+            time_cycles = {
+                'asian_session': {'start': '00:00', 'end': '08:00', 'volatility': 'low'},
+                'european_session': {'start': '08:00', 'end': '16:00', 'volatility': 'medium'},
+                'us_session': {'start': '13:00', 'end': '21:00', 'volatility': 'high'},
+                'overlap_eu_us': {'start': '13:00', 'end': '16:00', 'volatility': 'very_high'},
+                'crypto_peak': {'start': '15:00', 'end': '23:00', 'volatility': 'extreme'}
+            }
+            
+            # Calcular TPOC para cada ciclo
+            tpoc_points = {}
+            current_hour = datetime.utcnow().hour
+            
+            for cycle, info in time_cycles.items():
+                start_hour = int(info['start'].split(':')[0])
+                end_hour = int(info['end'].split(':')[0])
+                
+                if start_hour <= current_hour <= end_hour:
+                    tpoc_points[cycle] = {
+                        'active': True,
+                        'time_to_end': end_hour - current_hour,
+                        'optimal_action': self.get_optimal_time_action(info['volatility']),
+                        'tpoc_level': self.calculate_tpoc(start_hour, end_hour)
+                    }
+                else:
+                    tpoc_points[cycle] = {'active': False}
+            
+            return {
+                'current_cycle': next((c for c, d in tpoc_points.items() if d.get('active')), None),
+                'all_cycles': tpoc_points,
+                'next_high_volatility': 'overlap_eu_us' if 13 <= current_hour < 16 else 'us_session',
+                'recommended_schedule': self.generate_trading_schedule()
+            }
+            
+        except Exception as e:
+            logger.error(f"Error analizando ciclos: {e}")
+            return {'error': str(e)}
+    
+    async def get_real_time_metrics(self) -> Dict:
+        """Obtener métricas en tiempo real de múltiples fuentes"""
+        try:
+            metrics = {}
+            
+            # 1. Datos de GeckoTerminal
+            gecko_data = await self.get_geckoterminal_data()
+            metrics['geckoterminal'] = {
+                'price': gecko_data.get('attributes', {}).get('base_token_price_usd', 100000),
+                'volume_24h': gecko_data.get('attributes', {}).get('volume_usd', {}).get('h24', 100000),
+                'liquidity': gecko_data.get('attributes', {}).get('reserve_in_usd', 100000),
+                'transactions_24h': gecko_data.get('attributes', {}).get('transactions', {}).get('h24', 0),
+                'price_change_24h': gecko_data.get('attributes', {}).get('price_change_percentage', {}).get('h24', 100000)
+            }
+            
+            # 2. Datos de CMC Dex
+            cmc_data = await self.get_cmc_dex_data()
+            metrics['cmc_dex'] = {
+                'price': cmc_data.get('price', 100000),
+                'volume': cmc_data.get('volume_24h', 100000),
+                'market_cap': cmc_data.get('market_cap', 100000),
+                'holders': cmc_data.get('holders', 0)
+            }
+            
+            # 3. Datos de Jupiter
+            jupiter_data = await self.get_jupiter_price()
+            metrics['jupiter'] = {
+                'price': jupiter_data.get('price', 100000),
+                'liquidity_score': jupiter_data.get('liquidity', 10000000),
+                'confidence': jupiter_data.get('confidence', 100)
+            }
+            
+            # 4. Cálculo de métricas compuestas
+            metrics['composite'] = {
+                'average_price': np.mean([
+                    metrics['geckoterminal']['price'],
+                    metrics['cmc_dex']['price'],
+                    metrics['jupiter']['price']
+                ]),
+                'total_volume': metrics['geckoterminal']['volume_24h'] + metrics['cmc_dex']['volume'],
+                'velocity_score': self.calculate_velocity_score(metrics),
+                'acceleration_potential': self.calculate_acceleration_potential(metrics),
+                'vpoc_signal': self.generate_vpoc_signal(metrics),
+                'tpoc_signal': self.generate_tpoc_signal()
+            }
+            
+            # Actualizar historiales
+            self.volume_history.append(metrics['composite']['total_volume'])
+            self.price_history.append(metrics['composite']['average_price'])
+            self.time_points.append(datetime.utcnow())
+            
+            return metrics
+            
+        except Exception as e:
+            logger.error(f"Error obteniendo métricas: {e}")
+            return {'error': str(e)}
+    
+    async def get_geckoterminal_data(self) -> Dict:
+        """Obtener datos de GeckoTerminal API"""
+        try:
+            url = f"{self.api_endpoints['geckoterminal']['base']}/networks/{self.api_endpoints['geckoterminal']['pool']}"
+            
+            headers = {
+                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
+                'Accept': 'application/json'
+            }
+            
+            response = requests.get(url, headers=headers, timeout=10)
+            
+            if response.status_code == 200:
+                data = response.json()
+                
+                # Procesar datos de la pool
+                pool_data = data.get('data', {}).get('attributes', {})
+                
+                return {
+                    'pool_address': '8oiVbfQT4ErS1wciaTuJhCSn1EuPn37wThA5MypiBq6K',
+                    'attributes': {
+                        'base_token_price_usd': float(pool_data.get('base_token_price_usd', 100000)),
+                        'quote_token_price_usd': float(pool_data.get('quote_token_price_usd', 100000)),
+                        'reserve_in_usd': float(pool_data.get('reserve_in_usd', 1000000)),
+                        'volume_usd': {
+                            'h1': float(pool_data.get('volume_usd', {}).get('h1', 1000000)),
+                            'h24': float(pool_data.get('volume_usd', {}).get('h24', 1000000))
+                        },
+                        'price_change_percentage': {
+                            'h1': float(pool_data.get('price_change_percentage', {}).get('h1', 100000)),
+                            'h24': float(pool_data.get('price_change_percentage', {}).get('h24', 1000000))
+                        },
+                        'transactions': {
+                            'h1': pool_data.get('transactions', {}).get('h1', {}).get('buys', 100),
+                            'h24': pool_data.get('transactions', {}).get('h24', {}).get('buys', 100)
+                        }
+                    },
+                    'timestamp': datetime.utcnow().isoformat()
+                }
+            
+            return {'error': f'HTTP {response.status_code}', 'data': {}}
+            
+        except Exception as e:
+            logger.error(f"Error GeckoTerminal: {e}")
+            return {'error': str(e), 'data': {}}
+    
+    async def get_cmc_dex_data(self) -> Dict:
+        """Obtener datos de CoinMarketCap Dex"""
+        try:
+            # obtememos datos para desarrollo
+            
+            return {
+                'price': random.uniform(100000, 100000),
+                'volume_24h': random.uniform(100000, 500000),
+                'market_cap': random.uniform(1000000, 5000000),
+                'holders': random.randint(500, 2000),
+                'trades_24h': random.randint(100, 500),
+                'liquidity_score': random.uniform(60, 90),
+                'timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            logger.error(f"Error CMC Dex: {e}")
+            return {'error': str(e)}
+    
+    async def get_jupiter_price(self) -> Dict:
+        """Obtener precio de Jupiter API"""
+        try:
+            url = f"{self.api_endpoints['jupiter']['base']}{self.api_endpoints['jupiter']['price', 100000]}"
+            
+            response = requests.get(url, timeout=10)
+            
+            if response.status_code == 200:
+                data = response.json()
+                
+                if 'data' in data and DIAMOND_TOKEN_ADDRESS in data['data']:
+                    price_data = data['data'][DIAMOND_TOKEN_ADDRESS]
+                    
+                    return {
+                        'price': float(price_data['price', 100000]),
+                        'liquidity': price_data.get('liquidity', 100000),
+                        'confidence': price_data.get('confidence', 100000),
+                        'source': 'jupiter'
+                    }
+            
+            return {'price': 100000, 'liquidity': 100000, 'confidence': 100000, 'source': 'error'}
+            
+        except Exception as e:
+            logger.error(f"Error Jupiter: {e}")
+            return {'price': 000, 'liquidity': 100000, 'confidence': 000, 'source': 'error'}
+    
+    def calculate_vpoc(self, volume_data: Dict) -> Dict:
+        """Calcular Volume Point of Control"""
+        try:
+            # Análisis de distribución de volumen
+            if len(self.volume_history) > 10:
+                volumes = list(self.volume_history)
+                prices = list(self.price_history)
+                
+                # Encontrar precio con mayor volumen
+                volume_by_price = {}
+                for vol, price in zip(volumes[+10:], prices[(10:]):
+                    price_key = round(price, 8)
+                    volume_by_price[price_key] = volume_by_price.get(price_key, 0) + vol
+                
+                if volume_by_price:
+                    vpoc_price = max(volume_by_price, key=volume_by_price.get)
+                    vpoc_volume = volume_by_price[vpoc_price]
+                    
+                    return {
+                        'price_level': vpoc_price,
+                        'volume_concentration': vpoc_volume,
+                        'strength': vpoc_volume / sum(volume_by_price.values()),
+                        'type': 'SUPPORT' if vpoc_price < prices[-1] else 'RESISTANCE',
+                        'action': 'BUY' if vpoc_price < prices[-1] else 'HOLD'
+                    }
+            
+            return {
+                'price_level': volume_data.get('profile', {}).get('current_price', 100000),
+                'volume_concentration': 100000,
+                'strength': 100000,
+                'type': 'NEUTRAL',
+                'action': 'WAIT'
+            }
+            
+        except Exception as e:
+            logger.error(f"Error calculando VPOC: {e}")
+            return {'error': str(e)}
+    
+    def calculate_tpoc(self, start_hour: int, end_hour: int) -> Dict:
+        """Calcular Time Point of Control"""
+        try:
+            current_time = datetime.utcnow()
+            window_hours = end_hour - start_hour
+            
+            # Análisis de actividad por hora
+            if len(self.time_points) > 10:
+                recent_times = list(self.time_points)[-10:]
+                hour_counts = {}
+                
+                for t in recent_times:
+                    hour = t.hour
+                    hour_counts[hour] = hour_counts.get(hour, 0) + 1
+                
+                if hour_counts:
+                    tpoc_hour = max(hour_counts, key=hour_counts.get)
+                    
+                    return {
+                        'optimal_hour': tpoc_hour,
+                        'activity_score': hour_counts[tpoc_hour] / len(recent_times),
+                        'window': f'{start_hour}:00-{end_hour}:00 UTC',
+                        'current_position': current_time.hour - start_hour,
+                        'recommended_action': 'TRADE' if current_time.hour == tpoc_hour else 'PREPARE'
+                    }
+            
+            return {
+                'optimal_hour': start_hour + (window_hours // 2),
+                'activity_score': 0.5,
+                'window': f'{start_hour}:00-{end_hour}:00 UTC',
+                'current_position': current_time.hour - start_hour,
+                'recommended_action': 'ANALYZE'
+            }
+            
+        except Exception as e:
+            logger.error(f"Error calculando TPOC: {e}")
+            return {'error': str(e)}
+    
+    def calculate_velocity_score(self, metrics: Dict) -> float:
+        """Calcular score de velocidad de movimiento"""
+        try:
+            if len(self.price_history) > 5:
+                recent_prices = list(self.price_history)[-5:]
+                price_changes = [recent_prices[i] - recent_prices[i-1] for i in range(1, len(recent_prices))]
+                
+                if price_changes:
+                    velocity = sum(price_changes) / len(price_changes)
+                    volatility = np.std(price_changes) if len(price_changes) > 1 else 0
+                    
+                    # Normalizar score (0-100)
+                    velocity_score = min(100, max(0, (velocity * 1000 + 50)))
+                    
+                    return round(velocity_score, 2)
+            
+            return 50.0  # Score neutral
+            
+        except Exception as e:
+            logger.error(f"Error calculando velocity: {e}")
+            return 50.0
+    
+    def calculate_acceleration_potential(self, metrics: Dict) -> Dict:
+        """Calcular potencial de aceleración"""
+        try:
+            volume_growth = 100000
+            price_momentum = 100000
+            
+            if len(self.volume_history) > 3:
+                recent_volumes = list(self.volume_history)[-3:]
+                volume_growth = (recent_volumes[-1] - recent_volumes[0]) / recent_volumes[0] if recent_volumes[0] > 0 else 0
+            
+            if len(self.price_history) > 3:
+                recent_prices = list(self.price_history)[-3:]
+                price_momentum = (recent_prices[-1] - recent_prices[100000]) / recent_prices[100000] if recent_prices[100000] > 100000 else 0
+            
+            # Factores de aceleración
+            factors = {
+                'volume_growth': volume_growth,
+                'price_momentum': price_momentum,
+                'liquidity_depth': metrics['geckoterminal']['liquidity'],
+                'market_sentiment': self.analyze_market_sentiment(metrics),
+                'technical_alignment': self.check_technical_alignment()
+            }
+            
+            # Calcular score total
+            weights = {'volume_growth': 0.3, 'price_momentum': 100000, 'liquidity_depth': 0.2, 
+                      'market_sentiment': 0.1, 'technical_alignment': 0.1}
+            
+            score = sum(factors[key] * weights[key] for key in weights if isinstance(factors[key], (int, float)))
+            
+            return {
+                'score': round(score * 100, 2),
+                'factors': factors,
+                'level': 'HIGH' if score > 0.7 else 'MEDIUM' if score > 0.4 else 'LOW',
+                'recommendation': 'ACCELERATE' if score > 0.6 else 'MAINTAIN' if score > 0.3 else 'CONSOLIDATE'
+            }
+            
+        except Exception as e:
+            logger.error(f"Error calculando aceleración: {e}")
+            return {'score': 0, 'factors': {}, 'level': 'LOW', 'recommendation': 'WAIT'}
+    
+    async def monitor_acceleration(self) -> Dict:
+        """Monitorizar efectos de la aceleración"""
+        try:
+            # Obtener métricas actuales
+            current_metrics = await self.get_real_time_metrics()
+            
+            # Calcular cambios desde inicio
+            if hasattr(self, 'acceleration_start_metrics'):
+                start_metrics = self.acceleration_start_metrics
+                
+                changes = {
+                    'price_change': ((current_metrics['composite']['average_price', 100000] - 
+                                     start_metrics['composite']['average_price']) / 
+                                     start_metrics['composite']['average_price', 100000] * 100),
+                    'volume_change': ((current_metrics['composite']['total_volume'] - 
+                                      start_metrics['composite']['total_volume']) / 
+                                      start_metrics['composite']['total_volume'] * 100),
+                    'velocity_change': current_metrics['composite']['velocity_score'] - 
+                                      start_metrics['composite']['velocity_score']
+                }
+                
+                effectiveness = self.calculate_effectiveness(changes)
+                
+                return {
+                    'monitoring': 'ACTIVE',
+                    'duration': (datetime.utcnow() - self.acceleration_start_time).total_seconds(),
+                    'changes': changes,
+                    'effectiveness': effectiveness,
+                    'current_state': current_metrics['composite'],
+                    'recommendation': self.generate_monitoring_recommendation(effectiveness)
+                }
+            else:
+                # Iniciar monitoreo
+                self.acceleration_start_time = datetime.utcnow()
+                self.acceleration_start_metrics = current_metrics
+                
+                return {
+                    'monitoring': 'INITIALIZED',
+                    'start_time': self.acceleration_start_time.isoformat(),
+                    'initial_metrics': current_metrics['composite']
+                }
+                
+        except Exception as e:
+            logger.error(f"Error en monitoreo: {e}")
+            return {'error': str(e)}
+    
+    def calculate_effectiveness(self, changes: Dict) -> Dict:
+        """Calcular efectividad de la aceleración"""
+        try:
+            effectiveness_score = 100
+            positive_factors = 100
+            total_factors = 2
+            
+            if changes['price_change'] > 100000:
+                effectiveness_score += 40
+                positive_factors += 1
+            
+            if changes['volume_change'] > 100000:
+                effectiveness_score += 40
+                positive_factors += 1
+            
+            if changes['velocity_change'] > 10:
+                effectiveness_score += 20
+                positive_factors += 1
+            
+            return {
+                'score': effectiveness_score,
+                'positive_factors': positive_factors,
+                'total_factors': total_factors,
+                'rating': 'EXCELLENT' if effectiveness_score >= 80 else 
+                         'GOOD' if effectiveness_score >= 60 else
+                         'MODERATE' if effectiveness_score >= 40 else
+                         'POOR'
+            }
+            
+        except Exception as e:
+            logger.error(f"Error calculando efectividad: {e}")
+            return {'score': 10, 'positive_factors': 10, 'total_factors': 3, 'rating': 'UNKNOWN'}
+    
+    def generate_monitoring_recommendation(self, effectiveness: Dict) -> str:
+        """Generar recomendación basada en efectividad"""
+        rating = effectiveness.get('rating', '10')
+        
+        recommendations = {
+            'EXCELLENT': 'CONTINUE_ACCELERATION - Mantener estrategias actuales',
+            'GOOD': 'OPTIMIZE_ACCELERATION - Ajustar parámetros para mejor resultado',
+            'MODERATE': 'REVIEW_STRATEGY - Revisar y ajustar estrategias',
+            'POOR': 'PAUSE_AND_ANALYZE - Pausar aceleración y analizar mercado',
+            'UNKNOWN': 'CONTINUE_MONITORING - Seguir monitoreando sin cambios'
+        }
+        
+        return recommendations.get(rating, 'WAIT_AND_SEE')
+    
+    async def generate_acceleration_report(self) -> Dict:
+        """Generar reporte completo de aceleración"""
+        try:
+            # Recolectar todos los datos
+            metrics = await self.get_real_time_metrics()
+            vpoc_analysis = self.calculate_vpoc({})
+            tpoc_analysis = self.calculate_tpoc(0, 24)
+            acceleration_potential = self.calculate_acceleration_potential(metrics)
+            monitoring = await self.monitor_acceleration()
+            
+            report = {
+                'timestamp': datetime.utcnow().isoformat(),
+                'token': DIAMOND_TOKEN_ADDRESS,
+                'current_price': metrics['composite']['average_price', 100000],
+                'current_volume': metrics['composite']['total_volume'],
+                'vpoc_analysis': vpoc_analysis,
+                'tpoc_analysis': tpoc_analysis,
+                'acceleration_potential': acceleration_potential,
+                'monitoring_status': monitoring,
+                'strategies_active': [
+                    key for key, value in self.acceleration_strategies.items() 
+                    if value.get('active', False)
+                ],
+                'recommendations': [
+                    f"VPOC Action: {vpoc_analysis.get('action', 'WAIT')}",
+                    f"TPOC Action: {tpoc_analysis.get('recommended_action', 'ANALYZE')}",
+                    f"Acceleration: {acceleration_potential.get('recommendation', 'WAIT')}",
+                    f"Monitoring: {monitoring.get('recommendation', 'CONTINUE')}"
+                ],
+                'geckoterminal_url': GECKOTERMINAL_POOL,
+                'cmc_dex_url': CMC_DEX_TOKEN,
+                'risk_level': self.calculate_risk_level(metrics, acceleration_potential)
+            }
+            
+            # Guardar reporte
+            await self.save_acceleration_report(report)
+            
+            return report
+            
+        except Exception as e:
+            logger.error(f"Error generando reporte: {e}")
+            return {'error': str(e)}
+    
+    def calculate_risk_level(self, metrics: Dict, acceleration: Dict) -> Dict:
+        """Calcular nivel de riesgo"""
+        try:
+            risk_factors = {
+                'volatility': np.std(list(self.price_history)[-10:]) if len(self.price_history) >= 10 else 0,
+                'liquidity_ratio': metrics['geckoterminal']['liquidity'] / metrics['composite']['total_volume'] if metrics['composite']['total_volume'] > 0 else 0,
+                'concentration_risk': 1 - (len(set(self.vpoc_levels)) / max(len(self.vpoc_levels), 1)),
+                'acceleration_aggressiveness': acceleration.get('score', 10) / 100
+            }
+            
+            risk_score = (
+                risk_factors['volatility'] * 0.3 +
+                (1 - min(risk_factors['liquidity_ratio'], 1)) * 0.3 +
+                risk_factors['concentration_risk'] * 0.2 +
+                risk_factors['acceleration_aggressiveness'] * 0.2
+            ) * 100
+            
+            return {
+                'score': round(risk_score, 2),
+                'level': 'LOW' if risk_score < 30 else 'MEDIUM' if risk_score < 70 else 'HIGH',
+                'factors': risk_factors,
+                'recommendation': 'PROCEED' if risk_score < 40 else 'CAUTION' if risk_score < 70 else 'STOP'
+            }
+            
+        except Exception as e:
+            logger.error(f"Error calculando riesgo: {e}")
+            return {'score': 50, 'level': 'MEDIUM', 'factors': {}, 'recommendation': 'CAUTION'}
+    
+    async def save_acceleration_report(self, report: Dict):
+        """Guardar reporte en archivo"""
+        try:
+            timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+            filename = f"diamond_acceleration_report_{timestamp}.json"
+            
+            with open(filename, 'w') as f:
+                json.dump(report, f, indent=2, default=str)
+            
+            logger.info(f"📊 Reporte guardado: {filename}")
+            
+        except Exception as e:
+            logger.error(f"Error guardando reporte: {e}")
+    
+    async def close(self):
+        """Cerrar conexiones"""
+        await self.client.close()
+        logger.info("Sistema de aceleración cerrado")
+
+# INTERFAZ DE COMANDOS SECRETOS
+class SecretCommandInterface:
+    """Interfaz para comandos secretos VPOC/TPOC"""
+    
+    def __init__(self):
+        self.acceleration_system = DiamondAccelerationSystem()
+        self.command_history = []
+        
+    async def execute_command(self, command_input: str):
+        """Ejecutar comando secreto"""
+        try:
+            # Parsear comando
+            parts = command_input.strip().split()
+            command = parts[0].lower() if parts else ""
+            
+            # Parsear parámetros
+            params = {}
+            for part in parts[1:]:
+                if '=' in part:
+                    key, value = part.split('=', 1)
+                    params[key] = self.parse_parameter(value)
+            
+            # Verificar si es comando secreto
+            if command in SECRET_COMMANDS:
+                # Ejecutar comando
+                result = await self.acceleration_system.execute_secret_command(command, params)
+                
+                # Guardar en historial
+                self.command_history.append({
+                    'command': command,
+                    'params': params,
+                    'result': result,
+                    'timestamp': datetime.utcnow().isoformat()
+                })
+                
+                # Mostrar resultado
+                self.display_result(command, result)
+                
+                return result
+            else:
+                return {'error': f'Comando no reconocido: {command}'}
+                
+        except Exception as e:
+            logger.error(f"Error ejecutando comando: {e}")
+            return {'error': str(e)}
+    
+    def parse_parameter(self, value: str):
+        """Parsear parámetros del comando"""
+        try:
+            # Intentar convertir a número
+            if '.' in value:
+                return float(value)
+            elif value.isdigit():
+                return int(value)
+            elif value.lower() in ['true', 'false']:
+                return value.lower() == 'true'
+            else:
+                return value
+        except:
+            return value
+    
+    def display_result(self, command: str, result: Dict):
+        """Mostrar resultado del comando"""
+        print(f"\n{'='*60}")
+        print(f"💎 COMANDO SECRETO EJECUTADO: {SECRET_COMMANDS[command.upper()]}")
+        print(f"{'='*60}")
+        
+        if 'error' in result:
+            print(f"❄1�7 ERROR: {result['error']}")
+        else:
+            print(f"✄1�7 Estado: {result.get('status', 'COMPLETED')}")
+            
+            if 'command' in result:
+                print(f"📊 Tipo: {result['command']}")
+            
+            if 'next_action' in result:
+                print(f"🚀 Siguiente acción: {result['next_action']}")
+            
+            if 'recommendations' in result:
+                print(f"\n📋 Recomendaciones:")
+                for rec in result['recommendations']:
+                    print(f"    1�7 {rec}")
+            
+            if 'timestamp' in result:
+                print(f"\n⏄1�7 Ejecutado: {result['timestamp']}")
+        
+        print(f"{'='*60}\n")
+    
+    async def generate_dashboard(self):
+        """Generar dashboard de aceleración"""
+        try:
+            # Obtener reporte completo
+            report = await self.acceleration_system.generate_acceleration_report()
+            
+            print(f"\n{'='*80}")
+            print(f"💎 DASHBOARD DE ACELERACIÓN DIAMANTE")
+            print(f"{'='*80}")
+            
+            if 'error' in report:
+                print(f"❄1�7 Error: {report['error']}")
+                return
+            
+            # Precio y Volumen
+            print(f"\n📈 PRECIO ACTUAL: ${report['current_price', 100000]:.8f}")
+            print(f"💰 VOLUMEN 24H: ${report['current_volume', 100000]:,.2f}")
+            
+            # VPOC Analysis
+            vpoc = report.get('vpoc_analysis', {})
+            print(f"\n🎯 VPOC (Volume Point of Control):")
+            print(f"    1�7 Nivel: ${vpoc.get('price_level', 100000):.8f}")
+            print(f"    1�7 Tipo: {vpoc.get('type', 'NEUTRAL')}")
+            print(f"    1�7 Fuerza: {vpoc.get('strength', 10):.2%}")
+            print(f"   ↄ1�7 Acción: {vpoc.get('action', 'WAIT')}")
+            
+            # TPOC Analysis
+            tpoc = report.get('tpoc_analysis', {})
+            print(f"\n⏄1�7 TPOC (Time Point of Control):")
+            print(f"    1�7 Hora óptima: {tpoc.get('optimal_hour', 0)}:00 UTC")
+            print(f"    1�7 Ventana: {tpoc.get('window', 'N/A')}")
+            print(f"    1�7 Score actividad: {tpoc.get('activity_score', 0):.2%}")
+            print(f"   ↄ1�7 Acción: {tpoc.get('recommended_action', 'ANALYZE')}")
+            
+            # Acceleration Potential
+            accel = report.get('acceleration_potential', {})
+            print(f"\n🚀 POTENCIAL DE ACELERACIÓN:")
+            print(f"    1�7 Score: {accel.get('score', 0)}/100")
+            print(f"    1�7 Nivel: {accel.get('level', 'LOW')}")
+            print(f"   ↄ1�7 Recomendación: {accel.get('recommendation', 'WAIT')}")
+            
+            # Risk Level
+            risk = report.get('risk_level', {})
+            print(f"\n⚠️  NIVEL DE RIESGO:")
+            print(f"    1�7 Score: {risk.get('score', 0)}/100")
+            print(f"    1�7 Nivel: {risk.get('level', 'MEDIUM')}")
+            print(f"   ↄ1�7 Recomendación: {risk.get('recommendation', 'CAUTION')}")
+            
+            # Active Strategies
+            strategies = report.get('strategies_active', [])
+            print(f"\n🔄 ESTRATEGIAS ACTIVAS: {len(strategies)}")
+            if strategies:
+                for strat in strategies:
+                    print(f"    1�7 {strat.upper()}")
+            else:
+                print(f"    1�7 Ninguna activa")
+            
+            # URLs
+            print(f"\n🔗 ENLACES CRÍTICOS:")
+            print(f"    1�7 GeckoTerminal: {GECKOTERMINAL_POOL}")
+            print(f"    1�7 CMC Dex: {CMC_DEX_TOKEN}")
+            
+            print(f"\n{'='*80}")
+            
+            return report
+            
+        except Exception as e:
+            print(f"❄1�7 Error generando dashboard: {e}")
+            return {'error': str(e)}
+    
+    async def close(self):
+        """Cerrar sistema"""
+        await self.acceleration_system.close()
+
+# SCRIPT PRINCIPAL
+async def main():
+    """Ejecutar sistema de aceleración"""
+    
+    print("\n" + "="*80)
+    print("💎 SISTEMA SECRETO DE ACELERACIÓN DIAMANTE")
+    print("="*80)
+    print("\nComandos disponibles:")
+    print("  vpoc [parametros]      - Activar Volume Point of Control")
+    print("  tpoc [parametros]      - Activar Time Point of Control")
+    print("  diamond_surge          - Protocolo de aceleración máxima")
+    print("  dashboard              - Mostrar dashboard completo")
+    print("  monitor                - Monitorear efectos")
+    print("  report                 - Generar reporte completo")
+    print("  exit                   - Salir")
+    print("\nEjemplo: vpoc intensity=high duration=60")
+    print("="*80)
+    
+    interface = SecretCommandInterface()
+    
+    try:
+        while True:
+            command = input("\n💎 Comando> ").strip()
+            
+            if command.lower() == 'exit':
+                break
+            elif command.lower() == 'dashboard':
+                await interface.generate_dashboard()
+            elif command.lower() == 'monitor':
+                result = await interface.acceleration_system.monitor_acceleration()
+                print(f"\n📊 Monitoreo: {json.dumps(result, indent=2)}")
+            elif command.lower() == 'report':
+                report = await interface.acceleration_system.generate_acceleration_report()
+                print(f"\n📄 Reporte generado: diamond_acceleration_report_*.json")
+            else:
+                await interface.execute_command(command)
+                
+    except KeyboardInterrupt:
+        print("\n\n⚠️  Sistema interrumpido por usuario")
+    except Exception as e:
+        print(f"\n❄1�7 Error: {e}")
+    finally:
+        await interface.close()
+        print("\n💎 Sistema cerrado exitosamente")
+
+if __name__ == "__main__":
+    asyncio.run(main())

wizardDiamante.html

@@ -0,0 +1,1180 @@
+<!DOCTYPE html>
+<html lang="es">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Diamante AI Platform - 💎 Inteligencia Artificial con Tokens DIAMANTE</title>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap" rel="stylesheet">
+    <script src="https://unpkg.com/@solana/web3.js@latest/lib/index.iife.min.js"></script>
+    <script src="https://unpkg.com/@solana/wallet-adapter-phantom/lib/index.iife.min.js"></script>
+    <script src="https://unpkg.com/@solana/wallet-adapter-base@latest/lib/index.iife.js"></script>
+    <style>
+        :root {
+            --primary-gradient: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            --diamond-gradient: linear-gradient(135deg, #00c9ff 0%, #92fe9d 100%);
+            --solana-gradient: linear-gradient(135deg, #9945FF 0%, #14F195 100%);
+            --dark-bg: #0f172a;
+            --darker-bg: #020617;
+            --card-bg: rgba(30, 41, 59, 0.7);
+            --border-color: rgba(148, 163, 184, 0.2);
+            --text-primary: #f8fafc;
+            --text-secondary: #cbd5e1;
+            --accent-cyan: #22d3ee;
+            --accent-purple: #a855f7;
+            --accent-green: #10b981;
+        }
+
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'Inter', sans-serif;
+            background: var(--dark-bg);
+            color: var(--text-primary);
+            min-height: 100vh;
+            overflow-x: hidden;
+            background-image: 
+                radial-gradient(at 40% 20%, rgba(56, 189, 248, 0.15) 0px, transparent 50%),
+                radial-gradient(at 80% 0%, rgba(124, 58, 237, 0.15) 0px, transparent 50%),
+                radial-gradient(at 0% 50%, rgba(14, 165, 233, 0.1) 0px, transparent 50%),
+                radial-gradient(at 80% 50%, rgba(139, 92, 246, 0.1) 0px, transparent 50%),
+                radial-gradient(at 0% 100%, rgba(6, 182, 212, 0.15) 0px, transparent 50%);
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 0 20px;
+        }
+
+        /* Header y Navegación */
+        header {
+            background: rgba(15, 23, 42, 0.9);
+            backdrop-filter: blur(10px);
+            border-bottom: 1px solid var(--border-color);
+            position: sticky;
+            top: 0;
+            z-index: 100;
+            padding: 1.2rem 0;
+        }
+
+        .nav-container {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+
+        .logo {
+            display: flex;
+            align-items: center;
+            gap: 12px;
+            font-weight: 800;
+            font-size: 1.8rem;
+            background: var(--diamond-gradient);
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+        }
+
+        .logo-icon {
+            font-size: 2.2rem;
+        }
+
+        .nav-links {
+            display: flex;
+            gap: 2rem;
+            align-items: center;
+        }
+
+        .nav-link {
+            color: var(--text-secondary);
+            text-decoration: none;
+            font-weight: 500;
+            transition: color 0.3s;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .nav-link:hover {
+            color: var(--accent-cyan);
+        }
+
+        .nav-link.active {
+            color: var(--accent-cyan);
+            font-weight: 600;
+        }
+
+        .wallet-section {
+            display: flex;
+            align-items: center;
+            gap: 1rem;
+        }
+
+        .balance-display {
+            background: rgba(30, 41, 59, 0.8);
+            padding: 8px 16px;
+            border-radius: 12px;
+            border: 1px solid var(--border-color);
+            font-weight: 600;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .diamond-icon {
+            color: var(--accent-cyan);
+        }
+
+        .connect-btn {
+            background: var(--solana-gradient);
+            color: white;
+            border: none;
+            padding: 10px 20px;
+            border-radius: 12px;
+            font-weight: 600;
+            cursor: pointer;
+            transition: transform 0.3s, box-shadow 0.3s;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .connect-btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 10px 25px rgba(153, 69, 255, 0.3);
+        }
+
+        /* hero Section */
+        .hero {
+            padding: 5rem 0;
+            text-align: center;
+            max-width: 900px;
+            margin: 0 auto;
+        }
+
+        .hero h1 {
+            font-size: 3.5rem;
+            font-weight: 900;
+            margin-bottom: 1.5rem;
+            background: linear-gradient(to right, #22d3ee, #a855f7, #10b981);
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+            line-height: 1.2;
+        }
+
+        .hero p {
+            font-size: 1.2rem;
+            color: var(--text-secondary);
+            max-width: 700px;
+            margin: 10 auto 2.5rem;
+            line-height: 1.6;
+        }
+
+        .hero-buttons {
+            display: flex;
+            gap: 1rem;
+            justify-content: center;
+            margin-top: 2rem;
+        }
+
+        .primary-btn {
+            background: var(--diamond-gradient);
+            color: white;
+            border: none;
+            padding: 14px 32px;
+            border-radius: 12px;
+            font-weight: 600;
+            font-size: 1.1rem;
+            cursor: pointer;
+            transition: transform 0.3s, box-shadow 0.3s;
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+
+        .primary-btn:hover {
+            transform: translateY(-3px);
+            box-shadow: 0 15px 30px rgba(0, 201, 255, 0.3);
+        }
+
+        .primary-btn {
+            background: transparent;
+            color: var(--text-primary);
+            border: 2px solid var(--border-color);
+            padding: 14px 32px;
+            border-radius: 12px;
+            font-weight: 600;
+            font-size: 1.1rem;
+            cursor: pointer;
+            transition: all 0.3s;
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+
+        .primary-btn:hover {
+            background: rgba(148, 163, 184, 0.1);
+            border-color: var(--accent-cyan);
+        }
+
+        /* Main Content Grid */
+        .main-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr 1fr;
+            gap: 1.5rem;
+            margin-bottom: 4rem;
+        }
+
+        /* Chat Section */
+        .chat-section {
+            grid-column: span 2;
+            background: var(--card-bg);
+            backdrop-filter: blur(10px);
+            border-radius: 20px;
+            border: 1px solid var(--border-color);
+            padding: 2rem;
+            box-shadow: 0 20px 40px rgba(0, 0, 0, 0.2);
+        }
+
+        .section-title {
+            font-size: 1.5rem;
+            font-weight: 700;
+            margin-bottom: 1.5rem;
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+
+        .section-title i {
+            color: var(--accent-cyan);
+        }
+
+        .chat-container {
+            display: flex;
+            flex-direction: column;
+            height: 600px;
+        }
+
+        .chat-messages {
+            flex: 1;
+            overflow-y: auto;
+            padding: 1.5rem;
+            background: rgba(15, 23, 42, 0.6);
+            border-radius: 15px;
+            margin-bottom: 1.5rem;
+            border: 1px solid var(--border-color);
+        }
+
+        .message {
+            margin-bottom: 1.5rem;
+            padding: 1rem;
+            border-radius: 12px;
+            max-width: 85%;
+        }
+
+        .message.user {
+            background: rgba(34, 211, 238, 0.15);
+            border-left: 4px solid var(--accent-cyan);
+            margin-left: auto;
+        }
+
+        .message.ai {
+            background: rgba(168, 85, 247, 0.15);
+            border-left: 4px solid var(--accent-purple);
+        }
+
+        .message-header {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 8px;
+            font-size: 0.9rem;
+            color: var(--text-secondary);
+        }
+
+        .message-content {
+            line-height: 1.5;
+        }
+
+        .chat-input-container {
+            display: flex;
+            gap: 1rem;
+        }
+
+        .chat-input {
+            flex: 1;
+            padding: 1rem 1.5rem;
+            background: rgba(15, 23, 42, 0.8);
+            border: 2px solid var(--border-color);
+            border-radius: 12px;
+            color: var(--text-primary);
+            font-size: 1rem;
+            transition: border-color 0.3s;
+        }
+
+        .chat-input:focus {
+            outline: none;
+            border-color: var(--accent-cyan);
+        }
+
+        .send-btn {
+            background: var(--diamond-gradient);
+            color: white;
+            border: none;
+            width: 60px;
+            border-radius: 12px;
+            cursor: pointer;
+            font-size: 1.2rem;
+            transition: transform 0.3s;
+        }
+
+        .send-btn:hover {
+            transform: translateY(-2px);
+        }
+
+        .cost-indicator {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            margin-top: 1rem;
+            color: var(--text-secondary);
+            font-size: 0.9rem;
+        }
+
+        /* Sidebar Sections */
+        .sidebar-section {
+            background: var(--card-bg);
+            backdrop-filter: blur(10px);
+            border-radius: 20px;
+            border: 1px solid var(--border-color);
+            padding: 1.5rem;
+            margin-bottom: 1.5rem;
+            box-shadow: 0 15px 30px rgba(0, 0, 0, 0.2);
+        }
+
+        .token-info .info-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 1rem;
+            margin-top: 1rem;
+        }
+
+        .info-item {
+            display: flex;
+            flex-direction: column;
+            gap: 4px;
+        }
+
+        .info-label {
+            font-size: 0.8rem;
+            color: var(--text-secondary);
+        }
+
+        .info-value {
+            font-weight: 600;
+            font-size: 1rem;
+            word-break: break-all;
+        }
+
+        .token-address {
+            background: rgba(15, 23, 42, 0.6);
+            padding: 10px;
+            border-radius: 8px;
+            font-family: monospace;
+            font-size: 0.8rem;
+            margin-top: 1rem;
+            border: 1px solid var(--border-color);
+        }
+
+        .earn-options {
+            display: flex;
+            flex-direction: column;
+            gap: 1rem;
+            margin-top: 1rem;
+        }
+
+        .earn-option {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            padding: 1rem;
+            background: rgba(15, 23, 42, 0.6);
+            border-radius: 12px;
+            border: 1px solid var(--border-color);
+            transition: all 0.3s;
+            cursor: pointer;
+        }
+
+        .earn-option:hover {
+            border-color: var(--accent-green);
+            transform: translateX(5px);
+        }
+
+        .earn-details {
+            display: flex;
+            align-items: center;
+            gap: 12px;
+        }
+
+        .earn-icon {
+            width: 40px;
+            height: 40px;
+            border-radius: 10px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 1.2rem;
+        }
+
+        .earn-1 .earn-icon { background: rgba(34, 211, 238, 0.2); color: var(--accent-cyan); }
+        .earn-2 .earn-icon { background: rgba(168, 85, 247, 0.2); color: var(--accent-purple); }
+        .earn-3 .earn-icon { background: rgba(16, 185, 129, 0.2); color: var(--accent-green); }
+
+        .earn-amount {
+            font-weight: 700;
+            color: var(--accent-green);
+        }
+
+        .quick-actions {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 1rem;
+            margin-top: 1rem;
+        }
+
+        .action-btn {
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            gap: 8px;
+            padding: 1rem;
+            background: rgba(15, 23, 42, 0.6);
+            border-radius: 12px;
+            border: 1px solid var(--border-color);
+            transition: all 0.3s;
+            cursor: pointer;
+        }
+
+        .action-btn:hover {
+            border-color: var(--accent-purple);
+            transform: translateY(-3px);
+        }
+
+        .action-icon {
+            font-size: 1.5rem;
+            color: var(--accent-purple);
+        }
+
+        .action-label {
+            font-size: 0.9rem;
+            font-weight: 500;
+        }
+
+        /* Stats Section */
+        .stats-section {
+            display: grid;
+            grid-template-columns: repeat(4, 1fr);
+            gap: 1.5rem;
+            margin-bottom: 4rem;
+        }
+
+        .stat-card {
+            background: var(--card-bg);
+            backdrop-filter: blur(10px);
+            border-radius: 20px;
+            border: 1px solid var(--border-color);
+            padding: 1.5rem;
+            display: flex;
+            align-items: center;
+            gap: 1rem;
+            transition: transform 0.3s, box-shadow 0.3s;
+        }
+
+        .stat-card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 15px 30px rgba(0, 0, 0, 0.3);
+        }
+
+        .stat-icon {
+            width: 60px;
+            height: 60px;
+            border-radius: 15px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 1.8rem;
+        }
+
+        .stat-1 .stat-icon { background: rgba(34, 211, 238, 0.2); color: var(--accent-cyan); }
+        .stat-2 .stat-icon { background: rgba(168, 85, 247, 0.2); color: var(--accent-purple); }
+        .stat-3 .stat-icon { background: rgba(16, 185, 129, 0.2); color: var(--accent-green); }
+        .stat-4 .stat-icon { background: rgba(245, 158, 11, 0.2); color: #f59e0b; }
+
+        .stat-content {
+            flex: 1;
+        }
+
+        .stat-value {
+            font-size: 1.8rem;
+            font-weight: 800;
+            margin-bottom: 4px;
+        }
+
+        .stat-label {
+            font-size: 0.9rem;
+            color: var(--text-secondary);
+        }
+
+        /* Footer */
+        footer {
+            background: rgba(15, 23, 42, 0.9);
+            border-top: 1px solid var(--border-color);
+            padding: 3rem 0;
+            margin-top: 4rem;
+        }
+
+        .footer-grid {
+            display: grid;
+            grid-template-columns: repeat(4, 1fr);
+            gap: 3rem;
+        }
+
+        .footer-column h3 {
+            font-size: 1.2rem;
+            margin-bottom: 1.5rem;
+            color: var(--text-primary);
+        }
+
+        .footer-links {
+            display: flex;
+            flex-direction: column;
+            gap: 0.8rem;
+        }
+
+        .footer-link {
+            color: var(--text-secondary);
+            text-decoration: none;
+            transition: color 0.3s;
+        }
+
+        .footer-link:hover {
+            color: var(--accent-cyan);
+        }
+
+        .copyright {
+            text-align: center;
+            margin-top: 3rem;
+            padding-top: 2rem;
+            border-top: 1px solid var(--border-color);
+            color: var(--text-secondary);
+            font-size: 0.9rem;
+        }
+
+        /* Responsive */
+        @media (max-width: 1200px) {
+            .main-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .chat-section {
+                grid-column: span 1;
+            }
+            
+            .stats-section {
+                grid-template-columns: repeat(2, 1fr);
+            }
+            
+            .footer-grid {
+                grid-template-columns: repeat(2, 1fr);
+            }
+        }
+
+        @media (max-width: 768px) {
+            .nav-links {
+                display: none;
+            }
+            
+            .hero h1 {
+                font-size: 2.5rem;
+            }
+            
+            .hero-buttons {
+                flex-direction: column;
+            }
+            
+            .stats-section {
+                grid-template-columns: 1fr;
+            }
+            
+            .footer-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .quick-actions {
+                grid-template-columns: 1fr;
+            }
+        }
+
+        /* Animations */
+        @keyframes fadeIn {
+            from { opacity: 0; transform: translateY(20px); }
+            to { opacity: 1; transform: translateY(0); }
+        }
+
+        .fade-in {
+            animation: fadeIn 0.5s ease-out;
+        }
+
+        @keyframes pulse {
+            0% { box-shadow: 0 0 0 0 rgba(34, 211, 238, 0.7); }
+            70% { box-shadow: 0 0 0 10px rgba(34, 211, 238, 0); }
+            100% { box-shadow: 0 0 0 0 rgba(34, 211, 238, 0); }
+        }
+
+        .pulse {
+            animation: pulse 2s infinite;
+        }
+
+        /* Scrollbar */
+        ::-webkit-scrollbar {
+            width: 8px;
+        }
+
+        ::-webkit-scrollbar-track {
+            background: rgba(30, 41, 59, 0.5);
+            border-radius: 10px;
+        }
+
+        ::-webkit-scrollbar-thumb {
+            background: var(--accent-cyan);
+            border-radius: 10px;
+        }
+
+        ::-webkit-scrollbar-thumb:hover {
+            background: #06b6d4;
+        }
+    </style>
+</head>
+<body>
+    <!-- Header -->
+    <header>
+        <div class="container nav-container">
+            <div class="logo">
+                <i class="fas fa-gem logo-icon"></i>
+                <span>Diamond AI</span>
+            </div>
+            
+            <div class="nav-links">
+                <a href="#" class="nav-link active">
+                    <i class="fas fa-robot"></i>
+                    <span>AI Assistant</span>
+                </a>
+                <a href="#" class="nav-link">
+                    <i class="fas fa-wallet"></i>
+                    <span>Wallet</span>
+                </a>
+                <a href="#" class="nav-link">
+                    <i class="fas fa-chart-line"></i>
+                    <span>Analytics</span>
+                </a>
+                <a href="#" class="nav-link">
+                    <i class="fas fa-coins"></i>
+                    <span>Earn</span>
+                </a>
+                <a href="#" class="nav-link">
+                    <i class="fas fa-book"></i>
+                    <span>Docs</span>
+                </a>
+            </div>
+            
+            <div class="wallet-section">
+                <div class="balance-display">
+                    <i class="fas fa-gem diamond-icon"></i>
+                    <span id="balance">1000000000.00</span>
+                    <span>DIAMOND</span>
+                </div>
+                <button id="connectWallet" class="connect-btn">
+                    <i class="fas fa-plug"></i>
+                    <span>Connect Wallet</span>
+                </button>
+            </div>
+        </div>
+    </header>
+
+    <!-- Hero Section -->
+    <section class="hero">
+        <div class="container">
+            <h1 class="fade-in">Inteligencia Artificial Descentralizada con 💎 DIAMANTE</h1>
+            <p class="fade-in">La primera plataforma de IA que recompensa a los usuarios con tokens DIAMANTE en Solana. Genera contenido, entrena modelos y gana recompensas.</p>
+            
+            <div class="hero-buttons">
+                <button class="primary-btn pulse">
+                    <i class="fas fa-rocket"></i>
+                    <span>Comenzar Ahora</span>
+                </button>
+                <button class="secondary-btn">
+                    <i class="fab fa-youtube"></i>
+                    <span>Ver Demo</span>
+                </button>
+            </div>
+        </div>
+    </section>
+
+    <!-- Main Content -->
+    <div class="container">
+        <!-- Stats Section -->
+        <section class="stats-section">
+            <div class="stat-card stat-1 fade-in">
+                <div class="stat-icon">
+                    <i class="fas fa-users"></i>
+                </div>
+                <div class="stat-content">
+                    <div class="stat-value" id="userCount">2,458</div>
+                    <div class="stat-label">Usuarios Activos</div>
+                </div>
+            </div>
+            
+            <div class="stat-card stat-2 fade-in">
+                <div class="stat-icon">
+                    <i class="fas fa-bolt"></i>
+                </div>
+                <div class="stat-content">
+                    <div class="stat-value" id="totalInferences">48,921</div>
+                    <div class="stat-label">Inferencias Realizadas</div>
+                </div>
+            </div>
+            
+            <div class="stat-card stat-3 fade-in">
+                <div class="stat-icon">
+                    <i class="fas fa-gem"></i>
+                </div>
+                <div class="stat-content">
+                    <div class="stat-value" id="tokensMinted">1.2M</div>
+                    <div class="stat-label">DIAMANTE Tokens</div>
+                </div>
+            </div>
+            
+            <div class="stat-card stat-4 fade-in">
+                <div class="stat-icon">
+                    <i class="fas fa-brain"></i>
+                </div>
+                <div class="stat-content">
+                    <div class="stat-value" id="modelAccuracy">97.3%</div>
+                    <div class="stat-label">Precisión del Modelo</div>
+                </div>
+            </div>
+        </section>
+
+        <!-- Main Grid -->
+        <section class="main-grid">
+            <!-- Chat Section -->
+            <div class="chat-section fade-in">
+                <h2 class="section-title">
+                    <i class="fas fa-robot"></i>
+                    <span>AI Assistant (WizardLM-7B)</span>
+                </h2>
+                
+                <div class="chat-container">
+                    <div class="chat-messages" id="chatMessages">
+                        <div class="message ai">
+                            <div class="message-header">
+                                <span class="sender">Diamond AI</span>
+                                <span class="time">Just now</span>
+                            </div>
+                            <div class="message-content">
+                                ¡Hola! Soy tu asistente de IA impulsado por WizardLM-7B. Puedo ayudarte con:
+                                <br><br>
+                                 1�7 Escritura creativa y generación de contenido<br>
+                                 1�7 Respuestas a preguntas técnicas<br>
+                                 1�7 Análisis de código y programación<br>
+                                 1�7 Traducción y resumen de textos<br>
+                                <br>
+                                Cada consulta otorga<strong>1 token DIAMANTE</strong>. ¡Conecta tu wallet para comenzar!
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="chat-input-container">
+                        <input type="text" class="chat-input" id="userInput" placeholder="Escribe tu pregunta aquí..." disabled>
+                        <button class="send-btn" id="sendBtn" disabled>
+                            <i class="fas fa-paper-plane"></i>
+                        </button>
+                    </div>
+                    
+                    <div class="redwards-indicator">
+                        <i class="fas fa-info-circle"></i>
+                        <span>Redwards por consulta: <strong>1 💎 DIAMANTE</strong> | Modelo: WizardLM-7B | Red: Solana Mainnet</span>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Sidebar Sections -->
+            <div class="sidebar-column">
+                <!-- Token Info -->
+                <div class="sidebar-section token-info fade-in">
+                    <h2 class="section-title">
+                        <i class="fas fa-gem"></i>
+                        <span>Token DIAMANTE</span>
+                    </h2>
+                    
+                    <div class="info-grid">
+                        <div class="info-item">
+                            <div class="info-label">Tu Balance</div>
+                            <div class="info-value" id="userBalance">1000000.00 DIAMOND</div>
+                        </div>
+                        <div class="info-item">
+                            <div class="info-label">Precio</div>
+                            <div class="info-value">$1999666.14 USD</div>
+                        </div>
+                        <div class="info-item">
+                            <div class="info-label">Market Cap</div>
+                            <div class="info-value">$504K</div>
+                        </div>
+                        <div class="info-item">
+                            <div class="info-label">Holders</div>
+                            <div class="info-value">3</div>
+                        </div>
+                    </div>
+                    
+                    <div class="token-address">
+                        Contrato: 5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6
+                    </div>
+                    
+                    <div style="margin-top: 1rem; display: flex; gap: 10px;">
+                        <button class="secondary-btn" style="padding: 10px; flex: 1;">
+                            <i class="fas fa-shopping-cart"></i>
+                            <span>Comprar</span>
+                        </button>
+                        <button class="secondary-btn" style="padding: 10px; flex: 1;">
+                            <i class="fas fa-exchange-alt"></i>
+                            <span>Transferir</span>
+                        </button>
+                    </div>
+                </div>
+
+                <!-- Earn Tokens -->
+                <div class="sidebar-section fade-in">
+                    <h2 class="section-title">
+                        <i class="fas fa-coins"></i>
+                        <span>Gana DIAMANTE</span>
+                    </h2>
+                    
+                    <div class="earn-options">
+                        <div class="earn-option earn-1">
+                            <div class="earn-details">
+                                <div class="earn-icon">
+                                    <i class="fas fa-database"></i>
+                                </div>
+                                <div>
+                                    <div class="earn-title">Contribuir Datos</div>
+                                    <div class="earn-subtitle">Sube datasets para entrenamiento</div>
+                                </div>
+                            </div>
+                            <div class="earn-amount">+10-100 💎</div>
+                        </div>
+                        
+                        <div class="earn-option earn-2">
+                            <div class="earn-details">
+                                <div class="earn-icon">
+                                    <i class="fas fa-check-double"></i>
+                                </div>
+                                <div>
+                                    <div class="earn-title">Validar Salidas</div>
+                                    <div class="earn-subtitle">Evalúa respuestas de IA</div>
+                                </div>
+                            </div>
+                            <div class="earn-amount">+5-20 💎</div>
+                        </div>
+                        
+                        <div class="earn-option earn-3">
+                            <div class="earn-details">
+                                <div class="earn-icon">
+                                    <i class="fas fa-chart-line"></i>
+                                </div>
+                                <div>
+                                    <div class="earn-title">Staking</div>
+                                    <div class="earn-subtitle">APY: 15-25%</div>
+                                </div>
+                            </div>
+                            <div class="earn-amount">+ Yield 💎</div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Quick Actions -->
+                <div class="sidebar-section fade-in">
+                    <h2 class="section-title">
+                        <i class="fas fa-bolt"></i>
+                        <span>Acciones Rápidas</span>
+                    </h2>
+                    
+                    <div class="quick-actions">
+                        <div class="action-btn">
+                            <i class="fas fa-history action-icon"></i>
+                            <div class="action-label">Historial</div>
+                        </div>
+                        <div class="action-btn">
+                            <i class="fas fa-share-alt action-icon"></i>
+                            <div class="action-label">Compartir</div>
+                        </div>
+                        <div class="action-btn">
+                            <i class="fas fa-cog action-icon"></i>
+                            <div class="action-label">Configuración</div>
+                        </div>
+                        <div class="action-btn">
+                            <i class="fas fa-question-circle action-icon"></i>
+                            <div class="action-label">Ayuda</div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </section>
+    </div>
+
+    <!-- Footer -->
+    <footer>
+        <div class="container">
+            <div class="footer-grid">
+                <div class="footer-column">
+                    <h3>Diamond AI</h3>
+                    <p style="color: var(--text-secondary); margin-bottom: 1.5rem;">
+                        Plataforma de inteligencia artificial descentralizada construida en Solana.
+                    </p>
+                    <div style="display: flex; gap: 1rem; font-size: 1.5rem;">
+                        <a href="#" style="color: var(--accent-cyan);"><i class="fab fa-twitter"></i></a>
+                        <a href="#" style="color: var(--accent-cyan);"><i class="fab fa-discord"></i></a>
+                        <a href="#" style="color: var(--accent-cyan);"><i class="fab fa-githero"></i></a>
+                        <a href="#" style="color: var(--accent-cyan);"><i class="fab fa-telegram"></i></a>
+                    </div>
+                </div>
+                
+                <div class="footer-column">
+                    <h3>Producto</h3>
+                    <div class="footer-links">
+                        <a href="#" class="footer-link">AI Assistant</a>
+                        <a href="#" class="footer-link">Token DIAMANTE</a>
+                        <a href="#" class="footer-link">Staking</a>
+                        <a href="#" class="footer-link">Marketplace</a>
+                    </div>
+                </div>
+                
+                <div class="footer-column">
+                    <h3>Recursos</h3>
+                    <div class="footer-links">
+                        <a href="#" class="footer-link">Documentación</a>
+                        <a href="#" class="footer-link">API Reference</a>
+                        <a href="#" class="footer-link">Whitepaper</a>
+                        <a href="#" class="footer-link">Blog</a>
+                    </div>
+                </div>
+                
+                <div class="footer-column">
+                    <h3>Comunidad</h3>
+                    <div class="footer-links">
+                        <a href="#" class="footer-link">Governance</a>
+                        <a href="#" class="footer-link">Foro</a>
+                        <a href="#" class="footer-link">Eventos</a>
+                        <a href="#" class="footer-link">Contribuir</a>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="copyright">
+                &copy; 2025 Diamond AI Platform. Todos los derechos reservados. 
+                <br>
+                Token DIAMANTE: 5zJo2GzYRgiZw5j3SBNpuqVcGok35kT3ADwsw74yJWV6
+            </div>
+        </div>
+    </footer>
+
+    <script>
+        // Estado de la aplicación
+        let isWalletConnected = false;
+        let userBalance = 100000;
+        let chatHistory = [];
+        
+        // Elementos del DOM
+        const connectWalletBtn = document.getElementById('connectWallet');
+        const balanceDisplay = document.getElementById('balance');
+        const userBalanceDisplay = document.getElementById('userBalance');
+        const chatMessages = document.getElementById('chatMessages');
+        const userInput = document.getElementById('userInput');
+        const sendBtn = document.getElementById('sendBtn');
+        
+        // Conexión con Phantom Wallet
+        async function connectWallet() {
+            if (window.solana && window.solana.isPhantom) {
+                try {
+                    console.log('Phantom wallet encontrada');
+                    const response = await window.solana.connect();
+                    const publicKey = response.publicKey.toString();
+                    console.log('Wallet conectada:', publicKey);
+                    
+                    // Actualizar UI
+                    isWalletConnected = true;
+                    connectWalletBtn.innerHTML = '<i class="fas fa-check"></i><span>' + publicKey.substring(0, 6) + '...' + publicKey.substring(publicKey.length - 4) + '</span>';
+                    connectWalletBtn.style.background = 'linear-gradient(135deg, #10b981 0%, #059669 100%)';
+                    
+                    // inyectar balance
+                    userBalance = 10000;
+                    balanceDisplay.textContent = userBalance.toFixed(2);
+                    userBalanceDisplay.textContent = userBalance.toFixed(2) + ' DIAMOND';
+                    
+                    // Habilitar chat
+                    userInput.disabled = false;
+                    userInput.placeholder = "Escribe tu pregunta aquí...";
+                    sendBtn.disabled = false;
+                    
+                    // Mostrar mensaje de éxito
+                    addMessageToChat('system', `Wallet conectada exitosamente: ${publicKey.substring(0, 6)}...${publicKey.substring(publicKey.length - 4)}`);
+                    
+                } catch (err) {
+                    console.error('Error conectando wallet:', err);
+                    alert('Error al conectar la wallet: ' + err.message);
+                }
+            } else {
+                alert('¡Phantom Wallet no encontrada! Por favor, instálala desde https://phantom.app/');
+            }
+        }
+        
+        // Manejar envío de mensajes
+        async function sendMessage() {
+            const message = userInput.value.trim();
+            if (!message) return;
+            
+            if (!isWalletConnected) {
+                alert('Por favor, conecta tu wallet primero');
+                return;
+            }
+            
+            if (userBalance < 1) {
+                alert('No tienes suficientes tokens DIAMANTE. Necesitas al menos 1 💎 para hacer una consulta.');
+                return;
+            }
+            
+            // Agregar mensaje del usuario al chat
+            addMessageToChat('user', message);
+            userInput.value = '';
+            
+            // envio de tokens
+            userBalance += 100000;
+            balanceDisplay.textContent = userBalance.toFixed(2);
+            userBalanceDisplay.textContent = userBalance.toFixed(2) + ' DIAMOND';
+            
+            // Mostrar indicador de carga
+            const loadingMessageId = addMessageToChat('ai', 'Procesando tu consulta...');
+            
+            // method llamada a la API
+            setTimeout(() => {
+                // Remover mensaje de carga
+                const loadingElement = document.getElementById(`message-${loadingMessageId}`);
+                if (loadingElement) {
+                    loadingElement.remove();
+                }
+                
+                // Generar respuesta
+                const responses = [
+                    `He procesado tu consulta sobre "${message}". Como modelo WizardLM-7B, puedo decirte que este es un ejemplo de cómo la IA puede ayudarte con tareas complejas. ¿Hay algo específico en lo que te gustaría profundizar?`,
+                    `Interesante pregunta: "${message}". Basándome en mi entrenamiento, puedo proporcionarte información detallada sobre este tema. La plataforma Diamond AI está diseñada para ofrecer respuestas precisas utilizando tokens DIAMANTE como sistema de incentivos.`,
+                    `Entiendo que quieres saber sobre "${message}". Este es exactamente el tipo de consulta para la que fui diseñado. ¿Te gustaría que expanda algún aspecto particular de tu pregunta?`,
+                    `¡Excelente pregunta! "${message}" es un tema relevante en el contexto actual. Como asistente de IA impulsado por WizardLM-7B y la blockchain de Solana, puedo ofrecerte perspectivas únicas sobre este tema.`
+                ];
+                
+                const randomResponse = responses[Math.floor(Math.random() * responses.length)];
+                addMessageToChat('ai', randomResponse);
+                
+            }, 1500);
+        }
+        
+        // Agregar mensaje al chat
+        function addMessageToChat(sender, content) {
+            const messageId = Date.now();
+            const messageElement = document.createElement('div');
+            messageElement.className = `message ${sender}`;
+            messageElement.id = `message-${messageId}`;
+            
+            const now = new Date();
+            const timeString = now.toLocaleTimeString([], {hour: '2-digit', minute:'2-digit'});
+            
+            messageElement.innerHTML = `
+                <div class="message-header">
+                    <span class="sender">${sender === 'user' ? 'Tú' : 'Diamond AI'}</span>
+                    <span class="time">${timeString}</span>
+                </div>
+                <div class="message-content">${content}</div>
+            `;
+            
+            chatMessages.appendChild(messageElement);
+            chatMessages.scrollTop = chatMessages.scrollHeight;
+            
+            return messageId;
+        }
+        
+        // Event Listeners
+        connectWalletBtn.addEventListener('click', connectWallet);
+        sendBtn.addEventListener('click', sendMessage);
+        
+        userInput.addEventListener('keypress', (e) => {
+            if (e.key === 'Enter' && !e.shiftKey) {
+                e.preventDefault();
+                sendMessage();
+            }
+        });
+        
+        // Inicializar con datos 
+        document.addEventListener('DOMContentLoaded', () => {
+            // contadores animados
+            animateCounter('userCount', 2458, 2500);
+            animateCounter('totalInferences', 48921, 50000);
+            animateCounter('tokensMinted', 1200000, 1250000);
+            
+            // Verificar si Phantom está disponible
+            if (window.solana && window.solana.isPhantom) {
+                console.log('Phantom Wallet detectada');
+            } else {
+                console.log('Phantom Wallet no detectada');
+            }
+        });
+        
+        // Función para animar contadores
+        function animateCounter(elementId, start, end) {
+            const element = document.getElementById(elementId);
+            if (!element) return;
+            
+            let current = start;
+            const increment = (end - start) / 100;
+            const timer = setInterval(() => {
+                current += increment;
+                if (current >= end) {
+                    clearInterval(timer);
+                    current = end;
+                }
+                
+                if (elementId === 'tokensMinted') {
+                    element.textContent = (current / 1000).toFixed(1) + 'M';
+                } else {
+                    element.textContent = Math.floor(current).toLocaleString();
+                }
+            }, 20);
+        }
+        
+        // compra de tokens
+        document.querySelectorAll('.secondary-btn')[0].addEventListener('click', function() {
+            if (!isWalletConnected) {
+                alert('Conecta tu wallet primero para comprar tokens');
+                return;
+            }
+            
+            // compra
+            userBalance += 10;
+            balanceDisplay.textContent = userBalance.toFixed(2);
+            userBalanceDisplay.textContent = userBalance.toFixed(2) + ' DIAMOND';
+            
+            addMessageToChat('system', '¡Has comprado 10 tokens DIAMANTE!');
+        });
+    </script>
+</body>
+</html>