class MEVTypes:
    """MEVの主要な種類"""

    FRONTRUNNING = "フロントランニング"     # 他者の取引を先回り
    BACKRUNNING = "バックランニング"        # 大口取引の後に便乗
    SANDWICH_ATTACK = "サンドイッチ攻撃"    # 取引を挟み込む
    LIQUIDATION = "清算"                   # 清算機会の競争
    ARBITRAGE = "アービトラージ"           # 価格差の利用

# フロントランニングの典型的なフロー
def frontrunning_scenario():
    """
    1. 被害者が大口買い注文を送信
    2. MEVボットがメモリプールで検出
    3. MEVボットがより高いガス価格で同じ取引を先に実行
    4. 価格が上昇
    5. 被害者の注文が不利な価格で約定
    """
    pass

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from collections import deque
import asyncio

class MempoolAnalyzer:
    """メモリプール監視とMEV検出"""

    def __init__(self, window_size=100):
        self.window_size = window_size
        self.tx_queue = deque(maxlen=window_size)
        self.model = self._build_model()

    def _build_model(self):
        """MEV検出モデルの構築"""
        return RandomForestClassifier(
            n_estimators=100,
            max_depth=10,
            min_samples_split=5
        )

    def extract_features(self, transaction):
        """トランザクションから特徴量抽出"""
        features = {
            'gas_price': transaction['gasPrice'],
            'gas_price_ratio': transaction['gasPrice'] / self.avg_gas_price,
            'value': transaction['value'],
            'to_address_frequency': self.address_frequency.get(transaction['to'], 0),
            'similar_tx_count': self.count_similar_transactions(transaction),
            'time_since_last_similar': self.time_since_similar(transaction),
            'nonce_gap': self.check_nonce_gap(transaction['from']),
            'is_contract_interaction': self.is_contract(transaction['to'])
        }
        return np.array(list(features.values()))

    def detect_mev_pattern(self, transactions):
        """MEVパターンの検出"""
        patterns = {
            'sandwich': self._detect_sandwich_attack(transactions),
            'frontrun': self._detect_frontrunning(transactions),
            'backrun': self._detect_backrunning(transactions)
        }
        return patterns

    def _detect_sandwich_attack(self, transactions):
        """サンドイッチ攻撃の検出"""
        for i in range(1, len(transactions) - 1):
            tx_before = transactions[i-1]
            tx_victim = transactions[i]
            tx_after = transactions[i+1]

            # 同一アドレスからの取引で挟まれているか
            if (tx_before['from'] == tx_after['from'] and 
                tx_before['from'] != tx_victim['from']):

                # 取引方向が逆か確認
                if self._is_opposite_trade(tx_before, tx_after):
                    return {
                        'detected': True,
                        'victim_tx': tx_victim,
                        'attacker': tx_before['from'],
                        'profit_estimate': self._estimate_sandwich_profit(
                            tx_before, tx_victim, tx_after
                        )
                    }

        return {'detected': False}

import torch
import torch.nn as nn

class TransactionAnomalyDetector(nn.Module):
    """トランザクション異常検知モデル"""

    def __init__(self, input_dim=20, hidden_dim=64, latent_dim=16):
        super().__init__()

        # Encoder
        self.encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, latent_dim)
        )

        # Decoder
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim)
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

    def detect_anomaly(self, transaction_features, threshold=0.95):
        """異常スコアの計算"""
        with torch.no_grad():
            reconstructed = self.forward(transaction_features)
            mse = nn.functional.mse_loss(transaction_features, reconstructed, reduction='none')
            anomaly_score = torch.mean(mse, dim=1)

        return anomaly_score > threshold

class PrivatePoolManager:
    """プライベートメモリプールへの送信管理"""

    def __init__(self):
        self.flashbots_relay = "https://relay.flashbots.net"
        self.eden_network = "https://api.edennetwork.io"
        self.private_pools = {
            'flashbots': self.flashbots_relay,
            'eden': self.eden_network
        }

    async def send_private_transaction(self, transaction, pool='flashbots'):
        """プライベートプールへの送信"""
        # トランザクションの暗号化
        encrypted_tx = self.encrypt_transaction(transaction)

        # プライベートリレーへの送信
        response = await self.submit_to_relay(
            encrypted_tx, 
            self.private_pools[pool]
        )

        return response

    def estimate_mev_protection_cost(self, transaction):
        """MEV保護のコスト見積もり"""
        base_fee = transaction['gasPrice'] * transaction['gasLimit']
        protection_premium = base_fee * 0.05  # 5%のプレミアム

        return {
            'base_fee': base_fee,
            'protection_premium': protection_premium,
            'total_cost': base_fee + protection_premium
        }

class TransactionObfuscator:
    """取引の難読化とMEV対策"""

    def __init__(self):
        self.min_split_size = 0.1  # 最小分割サイズ（10%）
        self.max_splits = 5        # 最大分割数

    def split_order(self, order_size, price_impact_func):
        """注文の最適分割"""
        splits = []
        remaining = order_size

        while remaining > 0:
            # ランダムな分割サイズ
            split_ratio = np.random.uniform(self.min_split_size, 0.4)
            split_size = min(order_size * split_ratio, remaining)

            # 価格インパクトの評価
            impact = price_impact_func(split_size)

            splits.append({
                'size': split_size,
                'expected_impact': impact,
                'delay': np.random.uniform(1, 10)  # 1-10秒の遅延
            })

            remaining -= split_size

        return splits

    def add_noise_trades(self, main_trade):
        """ノイズ取引の追加"""
        noise_trades = []

        # メイン取引の10-20%のサイズでノイズ取引を生成
        for _ in range(np.random.randint(2, 5)):
            noise_size = main_trade['size'] * np.random.uniform(0.1, 0.2)
            noise_direction = np.random.choice(['buy', 'sell'])

            noise_trades.append({
                'size': noise_size,
                'direction': noise_direction,
                'timing': np.random.uniform(-30, 30)  # ±30秒
            })

        return noise_trades

class DynamicSlippageManager:
    """市場状況に応じた動的スリッページ設定"""

    def __init__(self):
        self.base_slippage = 0.003  # 0.3%
        self.mev_detection_model = MempoolAnalyzer()

    def calculate_optimal_slippage(self, market_conditions):
        """最適スリッページの計算"""

        # MEVリスクスコア
        mev_risk = self.mev_detection_model.assess_current_risk()

        # ボラティリティ調整
        volatility_multiplier = 1 + (market_conditions['volatility'] / 100)

        # 流動性調整
        liquidity_factor = 1 / np.sqrt(market_conditions['liquidity_score'])

        # 最終スリッページ計算
        optimal_slippage = (
            self.base_slippage * 
            (1 + mev_risk) * 
            volatility_multiplier * 
            liquidity_factor
        )

        return min(optimal_slippage, 0.05)  # 最大5%

    def adjust_for_order_size(self, base_slippage, order_size, market_depth):
        """注文サイズに基づくスリッページ調整"""
        size_impact = order_size / market_depth

        if size_impact > 0.1:  # 市場深度の10%以上
            return base_slippage * 2
        elif size_impact > 0.05:  # 5%以上
            return base_slippage * 1.5
        else:
            return base_slippage

class CommitRevealTrading:
    """コミット・リビール方式による取引"""

    def __init__(self):
        self.commitments = {}
        self.reveal_delay = 12  # ブロック数

    def create_commitment(self, order):
        """注文のコミットメント作成"""
        # 注文詳細のハッシュ化
        order_data = f"{order['type']}:{order['size']}:{order['price']}:{order['nonce']}"
        commitment = hashlib.sha256(order_data.encode()).hexdigest()

        # コミットメントの保存
        self.commitments[commitment] = {
            'order': order,
            'block_number': self.get_current_block(),
            'status': 'committed'
        }

        return commitment

    def reveal_order(self, commitment):
        """注文の公開と実行"""
        if commitment not in self.commitments:
            raise ValueError("Invalid commitment")

        commit_data = self.commitments[commitment]
        current_block = self.get_current_block()

        # 十分な時間が経過したか確認
        if current_block - commit_data['block_number'] < self.reveal_delay:
            raise ValueError("Too early to reveal")

        # 注文の実行
        return self.execute_order(commit_data['order'])

class TWAPExecutor:
    """TWAP戦略によるMEV耐性のある執行"""

    def __init__(self, total_duration=3600):  # 1時間
        self.total_duration = total_duration
        self.execution_history = []

    def create_execution_schedule(self, total_size, market_analysis):
        """実行スケジュールの作成"""

        # 市場の活発な時間帯を特定
        active_periods = market_analysis['active_periods']

        # 時間帯ごとの配分を計算
        time_weights = self.calculate_time_weights(active_periods)

        schedule = []
        for period, weight in time_weights.items():
            num_orders = max(1, int(weight * 20))  # 最大20分割

            for i in range(num_orders):
                schedule.append({
                    'time': period['start'] + (period['duration'] / num_orders) * i,
                    'size': total_size * weight / num_orders,
                    'randomization': np.random.uniform(-30, 30)  # ±30秒
                })

        return schedule

    def execute_with_mev_detection(self, order):
        """MEV検出機能付き執行"""
        # 執行前のMEVリスク評価
        mev_risk = self.assess_mev_risk()

        if mev_risk > 0.7:  # 高リスク
            # 執行を延期または分割
            return self.handle_high_mev_risk(order)
        else:
            return self.execute_order(order)

class MEVImpactAnalyzer:
    """MEVによる損失の測定と分析"""

    def __init__(self):
        self.execution_records = []

    def analyze_execution_quality(self, intended_order, actual_execution):
        """執行品質の分析"""

        metrics = {
            'intended_price': intended_order['price'],
            'executed_price': actual_execution['avg_price'],
            'slippage': self.calculate_slippage(intended_order, actual_execution),
            'mev_loss_estimate': self.estimate_mev_loss(intended_order, actual_execution),
            'execution_time': actual_execution['timestamp'] - intended_order['timestamp']
        }

        # サンドイッチ攻撃の検出
        if self.detect_sandwich_pattern(actual_execution):
            metrics['sandwich_detected'] = True
            metrics['estimated_attacker_profit'] = self.estimate_attacker_profit(
                actual_execution
            )

        return metrics

    def generate_mev_report(self, period='daily'):
        """MEV損失レポートの生成"""

        report = {
            'total_trades': len(self.execution_records),
            'mev_affected_trades': 0,
            'total_mev_loss': 0,
            'average_slippage': 0,
            'sandwich_attacks': 0
        }

        for record in self.execution_records:
            if record['mev_loss_estimate'] > 0:
                report['mev_affected_trades'] += 1
                report['total_mev_loss'] += record['mev_loss_estimate']

            if record.get('sandwich_detected'):
                report['sandwich_attacks'] += 1

        report['mev_rate'] = report['mev_affected_trades'] / report['total_trades']

        return report

class MultiLayerMEVDefense:
    """多層防御システム"""

    def __init__(self):
        self.defenses = {
            'layer1': PrivatePoolManager(),      # プライベートプール
            'layer2': TransactionObfuscator(),    # 取引難読化
            'layer3': DynamicSlippageManager(),   # 動的スリッページ
            'layer4': TWAPExecutor()              # TWAP実行
        }

    def execute_with_protection(self, order):
        """保護付き注文執行"""

        # Layer 1: MEVリスク評価
        mev_risk = self.assess_current_mev_risk()

        # Layer 2: 適切な防御手法の選択
        if mev_risk > 0.8:
            # 高リスク: プライベートプール使用
            return self.defenses['layer1'].send_private_transaction(order)

        elif mev_risk > 0.5:
            # 中リスク: 取引分割とTWAP
            splits = self.defenses['layer2'].split_order(order['size'])
            return self.defenses['layer4'].execute_schedule(splits)

        else:
            # 低リスク: 動的スリッページのみ
            protected_order = self.defenses['layer3'].apply_protection(order)
            return self.execute_standard(protected_order)

class AdaptiveMEVProtection:
    """適応的MEV保護システム"""

    def __init__(self):
        self.protection_history = []
        self.model = self.initialize_protection_model()

    def learn_from_execution(self, execution_result):
        """執行結果からの学習"""

        # 特徴量とラベルの抽出
        features = self.extract_execution_features(execution_result)
        label = 1 if execution_result['mev_detected'] else 0

        # モデルの更新
        self.model.partial_fit([features], [label])

        # 保護戦略の調整
        self.adjust_protection_parameters(execution_result)

    def adjust_protection_parameters(self, result):
        """保護パラメータの動的調整"""

        if result['mev_detected']:
            # MEVが検出された場合、保護を強化
            self.increase_protection_level()
        else:
            # コストと保護のバランスを最適化
            self.optimize_protection_cost()