#!/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"❄17 ERROR: {result['error']}") else: print(f"✄17 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"  17 {rec}") if 'timestamp' in result: print(f"\n⏄17 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"❄17 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"  17 Nivel: ${vpoc.get('price_level', 100000):.8f}") print(f"  17 Tipo: {vpoc.get('type', 'NEUTRAL')}") print(f"  17 Fuerza: {vpoc.get('strength', 10):.2%}") print(f" ↄ17 Acción: {vpoc.get('action', 'WAIT')}") # TPOC Analysis tpoc = report.get('tpoc_analysis', {}) print(f"\n⏄17 TPOC (Time Point of Control):") print(f"  17 Hora óptima: {tpoc.get('optimal_hour', 0)}:00 UTC") print(f"  17 Ventana: {tpoc.get('window', 'N/A')}") print(f"  17 Score actividad: {tpoc.get('activity_score', 0):.2%}") print(f" ↄ17 Acción: {tpoc.get('recommended_action', 'ANALYZE')}") # Acceleration Potential accel = report.get('acceleration_potential', {}) print(f"\n🚀 POTENCIAL DE ACELERACIÓN:") print(f"  17 Score: {accel.get('score', 0)}/100") print(f"  17 Nivel: {accel.get('level', 'LOW')}") print(f" ↄ17 Recomendación: {accel.get('recommendation', 'WAIT')}") # Risk Level risk = report.get('risk_level', {}) print(f"\n⚠️ NIVEL DE RIESGO:") print(f"  17 Score: {risk.get('score', 0)}/100") print(f"  17 Nivel: {risk.get('level', 'MEDIUM')}") print(f" ↄ17 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"  17 {strat.upper()}") else: print(f"  17 Ninguna activa") # URLs print(f"\n🔗 ENLACES CRÍTICOS:") print(f"  17 GeckoTerminal: {GECKOTERMINAL_POOL}") print(f"  17 CMC Dex: {CMC_DEX_TOKEN}") print(f"\n{'='*80}") return report except Exception as e: print(f"❄17 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❄17 Error: {e}") finally: await interface.close() print("\n💎 Sistema cerrado exitosamente") if __name__ == "__main__": asyncio.run(main())