#!/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())