vpoc.py

#!/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())