Create app.py

app.py

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+"""
+Cross-Asset Arbitrage Engine with Transformer Models
+Author: Spencer Purdy
+Description: A sophisticated arbitrage engine leveraging transformer models for price forecasting
+             across multiple asset classes and venues. Integrates CEX/DEX venues with latency-aware
+             execution, LLM-driven optimization, and comprehensive risk analytics.
+"""
+
+# Install required packages
+# !pip install -q transformers torch numpy pandas scikit-learn plotly gradio scipy statsmodels networkx
+
+# Core imports
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from datetime import datetime, timedelta
+import gradio as gr
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+import json
+import random
+from typing import Dict, List, Tuple, Optional, Any, Union
+from dataclasses import dataclass, field
+from collections import defaultdict, deque
+import warnings
+warnings.filterwarnings('ignore')
+
+# Additional imports
+from scipy import stats
+from scipy.optimize import minimize
+from sklearn.preprocessing import StandardScaler
+import networkx as nx
+
+# Set random seeds for reproducibility
+np.random.seed(42)
+torch.manual_seed(42)
+random.seed(42)
+
+# Configuration constants
+TRADING_DAYS_PER_YEAR = 365  # Crypto markets trade 24/7
+RISK_FREE_RATE = 0.045  # Current risk-free rate
+TRANSACTION_COST_CEX = 0.001  # 0.1% for centralized exchanges
+TRANSACTION_COST_DEX = 0.003  # 0.3% for decentralized exchanges
+GAS_COST_USD = 5.0  # Average gas cost for DEX transactions
+MIN_PROFIT_THRESHOLD = 0.002  # 0.2% minimum profit after costs
+MAX_POSITION_SIZE = 100000  # Maximum position size in USD
+LATENCY_CEX_MS = 50  # Average CEX latency in milliseconds
+LATENCY_DEX_MS = 1000  # Average DEX latency (block time)
+
+# Asset configuration
+ASSET_CLASSES = {
+    'crypto_spot': ['BTC', 'ETH', 'SOL', 'MATIC', 'AVAX'],
+    'crypto_futures': ['BTC-PERP', 'ETH-PERP', 'SOL-PERP'],
+    'fx_pairs': ['EUR/USD', 'GBP/USD', 'USD/JPY', 'AUD/USD'],
+    'equity_etfs': ['SPY', 'QQQ', 'IWM', 'EFA', 'EEM']
+}
+
+# Exchange configuration
+EXCHANGES = {
+    'cex': ['Binance', 'Coinbase', 'Kraken', 'FTX'],
+    'dex': ['Uniswap_V3', 'SushiSwap', 'Curve', 'Balancer']
+}
+
+@dataclass
+class OrderBook:
+    """Order book data structure for managing bid/ask levels"""
+    exchange: str
+    asset: str
+    timestamp: datetime
+    bids: List[Tuple[float, float]]  # List of (price, size) tuples
+    asks: List[Tuple[float, float]]  # List of (price, size) tuples
+
+    def get_best_bid(self) -> Tuple[float, float]:
+        """Get best bid price and size"""
+        return self.bids[0] if self.bids else (0.0, 0.0)
+
+    def get_best_ask(self) -> Tuple[float, float]:
+        """Get best ask price and size"""
+        return self.asks[0] if self.asks else (float('inf'), 0.0)
+
+    def get_mid_price(self) -> float:
+        """Calculate mid price from best bid and ask"""
+        bid, _ = self.get_best_bid()
+        ask, _ = self.get_best_ask()
+        return (bid + ask) / 2 if bid > 0 and ask < float('inf') else 0.0
+
+@dataclass
+class ArbitrageOpportunity:
+    """Data structure for storing identified arbitrage opportunities"""
+    opportunity_id: str
+    asset: str
+    buy_exchange: str
+    sell_exchange: str
+    buy_price: float
+    sell_price: float
+    max_size: float
+    expected_profit: float
+    expected_profit_pct: float
+    latency_risk: float
+    timestamp: datetime
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class ExecutionResult:
+    """Data structure for storing execution results"""
+    opportunity_id: str
+    success: bool
+    executed_size: float
+    buy_fill_price: float
+    sell_fill_price: float
+    realized_profit: float
+    slippage: float
+    latency_ms: float
+    gas_cost: float
+    timestamp: datetime
+
+class NumericalTransformer(nn.Module):
+    """Transformer architecture adapted for numerical price prediction with training capability"""
+
+    def __init__(self, input_dim: int = 10, hidden_dim: int = 128,
+                 num_heads: int = 8, num_layers: int = 4,
+                 prediction_horizon: int = 5):
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.prediction_horizon = prediction_horizon
+
+        # Input projection layer
+        self.input_projection = nn.Linear(input_dim, hidden_dim)
+
+        # Positional encoding for sequence position information
+        self.positional_encoding = self._create_positional_encoding(1000, hidden_dim)
+
+        # Transformer encoder for feature extraction
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=hidden_dim,
+            nhead=num_heads,
+            dim_feedforward=hidden_dim * 4,
+            dropout=0.1,
+            activation='gelu',
+            batch_first=True
+        )
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+
+        # Output heads for different prediction targets
+        self.price_head = nn.Linear(hidden_dim, prediction_horizon)
+        self.volatility_head = nn.Linear(hidden_dim, prediction_horizon)
+        self.volume_head = nn.Linear(hidden_dim, prediction_horizon)
+        self.uncertainty_head = nn.Linear(hidden_dim, prediction_horizon)
+
+        # Initialize weights
+        self._init_weights()
+
+    def _init_weights(self):
+        """Initialize model weights using Xavier initialization"""
+        for module in self.modules():
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+
+    def _create_positional_encoding(self, max_len: int, d_model: int) -> nn.Parameter:
+        """Create sinusoidal positional encoding"""
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() *
+                           (-np.log(10000.0) / d_model))
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+
+        return nn.Parameter(pe.unsqueeze(0), requires_grad=False)
+
+    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        """
+        Forward pass through the transformer
+        Args:
+            x: Input tensor of shape (batch_size, seq_len, input_dim)
+            mask: Optional attention mask
+        Returns:
+            Dictionary containing price, volatility, volume predictions and uncertainty
+        """
+        batch_size, seq_len, _ = x.shape
+
+        # Project input to hidden dimension
+        x = self.input_projection(x)
+
+        # Add positional encoding
+        x = x + self.positional_encoding[:, :seq_len, :]
+
+        # Pass through transformer encoder
+        encoded = self.transformer(x, src_key_padding_mask=mask)
+
+        # Use last sequence position for prediction
+        last_hidden = encoded[:, -1, :]
+
+        # Generate predictions from specialized heads
+        price_pred = self.price_head(last_hidden)
+        volatility_pred = torch.sigmoid(self.volatility_head(last_hidden)) * 0.1  # Scale to [0, 0.1]
+        volume_pred = torch.exp(self.volume_head(last_hidden))  # Ensure positive
+        uncertainty = torch.sigmoid(self.uncertainty_head(last_hidden))
+
+        return {
+            'price': price_pred,
+            'volatility': volatility_pred,
+            'volume': volume_pred,
+            'uncertainty': uncertainty
+        }
+
+class PriceForecastingEngine:
+    """Engine for multi-asset price forecasting using transformers with actual training"""
+
+    def __init__(self):
+        self.models = {}  # One model per asset class
+        self.scalers = {}  # Feature scalers
+        self.training_history = defaultdict(list)
+        self.is_trained = defaultdict(bool)
+
+        # Initialize models for each asset class
+        for asset_class in ASSET_CLASSES.keys():
+            self.models[asset_class] = NumericalTransformer()
+            self.scalers[asset_class] = StandardScaler()
+            self.is_trained[asset_class] = False
+
+    def prepare_features(self, price_data: pd.DataFrame) -> np.ndarray:
+        """Prepare features for transformer input"""
+        features = []
+
+        # Price features
+        features.append(price_data['close'].values)
+        features.append(price_data['high'].values)
+        features.append(price_data['low'].values)
+
+        # Volume features (log-transformed)
+        features.append(np.log1p(price_data['volume'].values))
+
+        # Technical indicators
+        returns = price_data['close'].pct_change().fillna(0)
+        features.append(returns.values)
+
+        # Moving averages
+        ma_7 = price_data['close'].rolling(7).mean().fillna(method='bfill')
+        ma_21 = price_data['close'].rolling(21).mean().fillna(method='bfill')
+        features.append((price_data['close'] / ma_7 - 1).values)
+        features.append((price_data['close'] / ma_21 - 1).values)
+
+        # Volatility (rolling standard deviation)
+        volatility = returns.rolling(20).std().fillna(method='bfill')
+        features.append(volatility.values)
+
+        # RSI (Relative Strength Index)
+        rsi = self.calculate_rsi(price_data['close'])
+        features.append(rsi.values / 100)  # Normalize to [0, 1]
+
+        # Order flow imbalance (simulated for this example)
+        ofi = np.random.normal(0, 0.1, len(price_data))
+        features.append(ofi)
+
+        # Stack features into matrix
+        feature_matrix = np.column_stack(features)
+
+        return feature_matrix
+
+    def calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series:
+        """Calculate Relative Strength Index"""
+        delta = prices.diff()
+        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
+        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
+
+        rs = gain / loss
+        rsi = 100 - (100 / (1 + rs))
+
+        return rsi.fillna(50)
+
+    def create_sequences(self, features: np.ndarray, targets: np.ndarray,
+                        seq_len: int = 50, horizon: int = 5) -> Tuple[np.ndarray, np.ndarray]:
+        """Create sequences for training the transformer"""
+        sequences = []
+        target_sequences = []
+
+        for i in range(seq_len, len(features) - horizon):
+            sequences.append(features[i-seq_len:i])
+            target_sequences.append(targets[i:i+horizon])
+
+        return np.array(sequences), np.array(target_sequences)
+
+    def train_model(self, asset_class: str, price_data: pd.DataFrame,
+                   epochs: int = 50, batch_size: int = 32):
+        """Train the transformer model on historical data"""
+        if len(price_data) < 100:
+            return  # Not enough data to train
+
+        # Prepare features
+        features = self.prepare_features(price_data)
+
+        # Scale features
+        features_scaled = self.scalers[asset_class].fit_transform(features)
+
+        # Prepare targets (future returns)
+        returns = price_data['close'].pct_change().fillna(0).values
+
+        # Create sequences
+        X, y = self.create_sequences(features_scaled, returns)
+
+        if len(X) == 0:
+            return  # Not enough sequences
+
+        # Convert to tensors
+        X_tensor = torch.FloatTensor(X)
+        y_tensor = torch.FloatTensor(y)
+
+        # Create data loader
+        dataset = torch.utils.data.TensorDataset(X_tensor, y_tensor)
+        loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+        # Setup training
+        model = self.models[asset_class]
+        optimizer = optim.Adam(model.parameters(), lr=0.001)
+        criterion = nn.MSELoss()
+
+        # Training loop
+        model.train()
+        for epoch in range(epochs):
+            epoch_loss = 0.0
+
+            for batch_X, batch_y in loader:
+                optimizer.zero_grad()
+
+                # Forward pass
+                predictions = model(batch_X)
+
+                # Calculate loss (using price predictions)
+                loss = criterion(predictions['price'], batch_y)
+
+                # Backward pass
+                loss.backward()
+                optimizer.step()
+
+                epoch_loss += loss.item()
+
+            # Record training history
+            avg_loss = epoch_loss / len(loader)
+            self.training_history[asset_class].append(avg_loss)
+
+        # Mark as trained
+        self.is_trained[asset_class] = True
+        model.eval()
+
+    def forecast_prices(self, asset: str, price_history: pd.DataFrame,
+                       horizon: int = 5) -> Dict[str, np.ndarray]:
+        """Generate price forecasts using transformer model"""
+
+        # Determine asset class
+        asset_class = self._get_asset_class(asset)
+        if not asset_class:
+            return self._generate_random_forecast(horizon)
+
+        # Train model if not already trained
+        if not self.is_trained[asset_class] and len(price_history) > 100:
+            self.train_model(asset_class, price_history)
+
+        # Prepare features
+        features = self.prepare_features(price_history)
+
+        # Scale features
+        features_scaled = self.scalers[asset_class].fit_transform(features)
+
+        # Create sequences
+        seq_len = min(50, len(features_scaled))
+        if len(features_scaled) < seq_len:
+            # Pad if necessary
+            padding = seq_len - len(features_scaled)
+            features_scaled = np.vstack([
+                np.zeros((padding, features_scaled.shape[1])),
+                features_scaled
+            ])
+
+        # Get recent sequence
+        sequence = features_scaled[-seq_len:].reshape(1, seq_len, -1)
+        sequence_tensor = torch.FloatTensor(sequence)
+
+        # Generate forecast
+        model = self.models[asset_class]
+        model.eval()
+
+        with torch.no_grad():
+            predictions = model(sequence_tensor)
+
+        # Extract predictions
+        current_price = price_history['close'].iloc[-1]
+
+        # Convert relative predictions to absolute prices
+        price_changes = predictions['price'].numpy()[0]
+        price_forecast = current_price * (1 + price_changes * 0.01)  # Scale predictions
+
+        return {
+            'price': price_forecast,
+            'volatility': predictions['volatility'].numpy()[0],
+            'volume': predictions['volume'].numpy()[0],
+            'uncertainty': predictions['uncertainty'].numpy()[0]
+        }
+
+    def _get_asset_class(self, asset: str) -> Optional[str]:
+        """Determine asset class for a given asset"""
+        for asset_class, assets in ASSET_CLASSES.items():
+            if asset in assets or any(asset.startswith(a) for a in assets):
+                return asset_class
+        return None
+
+    def _generate_random_forecast(self, horizon: int) -> Dict[str, np.ndarray]:
+        """Generate random forecast as fallback"""
+        return {
+            'price': np.random.normal(100, 2, horizon),
+            'volatility': np.random.uniform(0.01, 0.05, horizon),
+            'volume': np.random.lognormal(15, 0.5, horizon),
+            'uncertainty': np.random.uniform(0.3, 0.7, horizon)
+        }
+
+class ExchangeSimulator:
+    """Simulate exchange order books and execution with realistic market dynamics"""
+
+    def __init__(self, exchange_type: str = 'cex'):
+        self.exchange_type = exchange_type
+        self.order_books = {}
+        self.latency_ms = LATENCY_CEX_MS if exchange_type == 'cex' else LATENCY_DEX_MS
+        self.transaction_cost = TRANSACTION_COST_CEX if exchange_type == 'cex' else TRANSACTION_COST_DEX
+
+    def generate_order_book(self, asset: str, mid_price: float,
+                           spread_bps: float = 10, market_conditions: Dict[str, Any] = None) -> OrderBook:
+        """Generate realistic order book with dynamic spread based on market conditions"""
+
+        # Adjust spread based on market conditions
+        if market_conditions:
+            volatility = market_conditions.get('volatility', 0.02)
+            liquidity = market_conditions.get('liquidity', 1.0)
+            spread_bps *= (1 + volatility * 10) * (2 - liquidity)
+
+        spread = mid_price * spread_bps / 10000
+
+        # Generate bid/ask levels with realistic depth
+        n_levels = 10
+        bids = []
+        asks = []
+
+        for i in range(n_levels):
+            # Bid side
+            bid_price = mid_price - spread/2 - i * spread/10
+            bid_size = np.random.lognormal(10, 1) * (n_levels - i) / n_levels
+            bids.append((bid_price, bid_size))
+
+            # Ask side
+            ask_price = mid_price + spread/2 + i * spread/10
+            ask_size = np.random.lognormal(10, 1) * (n_levels - i) / n_levels
+            asks.append((ask_price, ask_size))
+
+        return OrderBook(
+            exchange=self.exchange_type,
+            asset=asset,
+            timestamp=datetime.now(),
+            bids=bids,
+            asks=asks
+        )
+
+    def simulate_market_impact(self, size: float, liquidity: float) -> float:
+        """Calculate market impact using square-root model"""
+        # Almgren-Chriss square-root market impact model
+        impact_bps = 10 * np.sqrt(size / liquidity)
+        return impact_bps / 10000
+
+    def execute_order(self, order_book: OrderBook, side: str,
+                     size: float) -> Tuple[float, float]:
+        """
+        Simulate order execution with realistic slippage
+        Returns: (fill_price, actual_size)
+        """
+
+        filled_size = 0
+        total_cost = 0
+
+        if side == 'buy':
+            # Execute against asks
+            for ask_price, ask_size in order_book.asks:
+                if filled_size >= size:
+                    break
+
+                fill_amount = min(size - filled_size, ask_size)
+                filled_size += fill_amount
+                total_cost += fill_amount * ask_price
+
+        else:  # sell
+            # Execute against bids
+            for bid_price, bid_size in order_book.bids:
+                if filled_size >= size:
+                    break
+
+                fill_amount = min(size - filled_size, bid_size)
+                filled_size += fill_amount
+                total_cost += fill_amount * bid_price
+
+        # Calculate average fill price
+        avg_fill_price = total_cost / filled_size if filled_size > 0 else 0
+
+        # Add market impact
+        liquidity = sum(s for _, s in order_book.bids) + sum(s for _, s in order_book.asks)
+        impact = self.simulate_market_impact(filled_size, liquidity)
+
+        if side == 'buy':
+            avg_fill_price *= (1 + impact)
+        else:
+            avg_fill_price *= (1 - impact)
+
+        return avg_fill_price, filled_size
+
+class ArbitrageDetector:
+    """Detect arbitrage opportunities across venues with advanced filtering"""
+
+    def __init__(self):
+        self.opportunity_history = []
+        self.min_profit_threshold = MIN_PROFIT_THRESHOLD
+
+    def find_opportunities(self, order_books: Dict[str, Dict[str, OrderBook]],
+                          transaction_costs: Dict[str, float],
+                          forecasts: Dict[str, Dict[str, np.ndarray]] = None) -> List[ArbitrageOpportunity]:
+        """Find arbitrage opportunities across all venues with forecast integration"""
+
+        opportunities = []
+
+        # Check each asset
+        for asset in self._get_all_assets(order_books):
+            asset_books = self._get_asset_order_books(order_books, asset)
+
+            if len(asset_books) < 2:
+                continue
+
+            # Find best bid and ask across all exchanges
+            best_bid_exchange, best_bid_price, best_bid_size = self._find_best_bid(asset_books)
+            best_ask_exchange, best_ask_price, best_ask_size = self._find_best_ask(asset_books)
+
+            if best_bid_price > best_ask_price:
+                # Calculate potential profit
+                max_size = min(best_bid_size, best_ask_size,
+                             MAX_POSITION_SIZE / best_ask_price)
+
+                # Calculate costs
+                buy_cost = transaction_costs.get(best_ask_exchange, TRANSACTION_COST_CEX)
+                sell_cost = transaction_costs.get(best_bid_exchange, TRANSACTION_COST_CEX)
+
+                # Add gas cost for DEX
+                gas_cost = 0
+                if 'dex' in best_ask_exchange.lower() or 'dex' in best_bid_exchange.lower():
+                    gas_cost = GAS_COST_USD
+
+                # Calculate profit
+                gross_profit = (best_bid_price - best_ask_price) * max_size
+                total_cost = (buy_cost + sell_cost) * best_ask_price * max_size + gas_cost
+                net_profit = gross_profit - total_cost
+                profit_pct = net_profit / (best_ask_price * max_size) if max_size > 0 else 0
+
+                # Adjust for price forecast if available
+                if forecasts and asset in forecasts:
+                    price_forecast = forecasts[asset]['price'][0]
+                    forecast_adjustment = (price_forecast - best_ask_price) / best_ask_price
+                    profit_pct += forecast_adjustment * 0.5  # Weight forecast impact
+
+                if profit_pct > self.min_profit_threshold:
+                    opportunity = ArbitrageOpportunity(
+                        opportunity_id=f"{asset}_{datetime.now().strftime('%Y%m%d_%H%M%S%f')}",
+                        asset=asset,
+                        buy_exchange=best_ask_exchange,
+                        sell_exchange=best_bid_exchange,
+                        buy_price=best_ask_price,
+                        sell_price=best_bid_price,
+                        max_size=max_size,
+                        expected_profit=net_profit,
+                        expected_profit_pct=profit_pct,
+                        latency_risk=self._calculate_latency_risk(
+                            best_ask_exchange, best_bid_exchange
+                        ),
+                        timestamp=datetime.now(),
+                        metadata={
+                            'spread': best_bid_price - best_ask_price,
+                            'gas_cost': gas_cost,
+                            'transaction_costs': buy_cost + sell_cost,
+                            'forecast_impact': forecast_adjustment if forecasts and asset in forecasts else 0
+                        }
+                    )
+
+                    opportunities.append(opportunity)
+                    self.opportunity_history.append(opportunity)
+
+        return opportunities
+
+    def _get_all_assets(self, order_books: Dict[str, Dict[str, OrderBook]]) -> set:
+        """Get all unique assets across exchanges"""
+        assets = set()
+        for exchange_books in order_books.values():
+            assets.update(exchange_books.keys())
+        return assets
+
+    def _get_asset_order_books(self, order_books: Dict[str, Dict[str, OrderBook]],
+                              asset: str) -> Dict[str, OrderBook]:
+        """Get order books for specific asset across exchanges"""
+        asset_books = {}
+        for exchange, books in order_books.items():
+            if asset in books:
+                asset_books[exchange] = books[asset]
+        return asset_books
+
+    def _find_best_bid(self, asset_books: Dict[str, OrderBook]) -> Tuple[str, float, float]:
+        """Find best bid across exchanges"""
+        best_exchange = None
+        best_price = 0
+        best_size = 0
+
+        for exchange, book in asset_books.items():
+            bid_price, bid_size = book.get_best_bid()
+            if bid_price > best_price:
+                best_exchange = exchange
+                best_price = bid_price
+                best_size = bid_size
+
+        return best_exchange, best_price, best_size
+
+    def _find_best_ask(self, asset_books: Dict[str, OrderBook]) -> Tuple[str, float, float]:
+        """Find best ask across exchanges"""
+        best_exchange = None
+        best_price = float('inf')
+        best_size = 0
+
+        for exchange, book in asset_books.items():
+            ask_price, ask_size = book.get_best_ask()
+            if ask_price < best_price:
+                best_exchange = exchange
+                best_price = ask_price
+                best_size = ask_size
+
+        return best_exchange, best_price, best_size
+
+    def _calculate_latency_risk(self, buy_exchange: str, sell_exchange: str) -> float:
+        """Calculate latency risk score (0-1)"""
+        # Higher risk for cross-exchange type arbitrage
+        if ('dex' in buy_exchange.lower()) != ('dex' in sell_exchange.lower()):
+            return 0.8  # High risk due to different settlement times
+        elif 'dex' in buy_exchange.lower():
+            return 0.6  # Medium risk for DEX-DEX
+        else:
+            return 0.3  # Lower risk for CEX-CEX
+
+class LLMStrategyOptimizer:
+    """LLM-inspired strategy parameter optimization with machine learning"""
+
+    def __init__(self):
+        self.parameter_history = defaultdict(list)
+        self.performance_history = []
+        self.current_parameters = self._get_default_parameters()
+        self.optimization_model = self._build_optimization_model()
+
+    def _get_default_parameters(self) -> Dict[str, Any]:
+        """Get default strategy parameters"""
+        return {
+            'min_profit_threshold': 0.002,
+            'max_position_size': 100000,
+            'risk_limit': 0.02,  # 2% per trade
+            'correlation_threshold': 0.7,
+            'rebalance_frequency': 300,  # seconds
+            'latency_buffer': 1.5,  # multiplier for latency estimates
+            'confidence_threshold': 0.6,
+            'max_concurrent_trades': 5
+        }
+
+    def _build_optimization_model(self) -> nn.Module:
+        """Build neural network for parameter optimization"""
+        class ParameterOptimizer(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.fc1 = nn.Linear(20, 64)  # Input features
+                self.fc2 = nn.Linear(64, 32)
+                self.fc3 = nn.Linear(32, 8)   # Output parameters
+                self.relu = nn.ReLU()
+                self.sigmoid = nn.Sigmoid()
+
+            def forward(self, x):
+                x = self.relu(self.fc1(x))
+                x = self.relu(self.fc2(x))
+                x = self.sigmoid(self.fc3(x))
+                return x
+
+        return ParameterOptimizer()
+
+    def generate_parameter_suggestions(self,
+                                     recent_performance: List[ExecutionResult],
+                                     market_conditions: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate parameter adjustments using ML-based optimization"""
+
+        suggestions = self.current_parameters.copy()
+
+        if not recent_performance:
+            return suggestions
+
+        # Extract performance features
+        success_rate = sum(1 for r in recent_performance if r.success) / len(recent_performance)
+        avg_slippage = np.mean([r.slippage for r in recent_performance])
+        avg_profit = np.mean([r.realized_profit for r in recent_performance])
+        profit_variance = np.var([r.realized_profit for r in recent_performance])
+
+        # Create feature vector
+        features = [
+            success_rate,
+            avg_slippage,
+            avg_profit / 1000,  # Normalize
+            profit_variance / 1000000,  # Normalize
+            market_conditions.get('volatility', 0.02),
+            market_conditions.get('liquidity', 1.0),
+            len(recent_performance) / 100,  # Normalize
+            self.current_parameters['min_profit_threshold'],
+            self.current_parameters['max_position_size'] / 1000000,
+            self.current_parameters['risk_limit'],
+            self.current_parameters['correlation_threshold'],
+            self.current_parameters['rebalance_frequency'] / 3600,
+            self.current_parameters['latency_buffer'],
+            self.current_parameters['confidence_threshold'],
+            self.current_parameters['max_concurrent_trades'] / 10,
+            # Additional market features
+            market_conditions.get('max_volatility', 0.03),
+            float(datetime.now().hour) / 24,  # Time of day
+            float(datetime.now().weekday()) / 7,  # Day of week
+            0.5,  # Placeholder for sentiment (would be real in production)
+            0.5   # Placeholder for market regime (would be real in production)
+        ]
+
+        # Use ML model for optimization
+        feature_tensor = torch.FloatTensor([features])
+
+        with torch.no_grad():
+            adjustments = self.optimization_model(feature_tensor).numpy()[0]
+
+        # Apply ML-suggested adjustments
+        suggestions['min_profit_threshold'] = 0.001 + adjustments[0] * 0.009
+        suggestions['max_position_size'] = 10000 + adjustments[1] * 490000
+        suggestions['risk_limit'] = 0.005 + adjustments[2] * 0.045
+        suggestions['correlation_threshold'] = 0.5 + adjustments[3] * 0.4
+        suggestions['rebalance_frequency'] = 60 + adjustments[4] * 3540
+        suggestions['latency_buffer'] = 1.0 + adjustments[5] * 2.0
+        suggestions['confidence_threshold'] = 0.5 + adjustments[6] * 0.4
+        suggestions['max_concurrent_trades'] = int(1 + adjustments[7] * 9)
+
+        # Rule-based adjustments on top of ML
+        if success_rate < 0.7:
+            suggestions['min_profit_threshold'] *= 1.1
+            suggestions['confidence_threshold'] *= 1.05
+
+        if avg_slippage > 0.001:
+            suggestions['max_position_size'] *= 0.9
+            suggestions['latency_buffer'] *= 1.1
+
+        if avg_profit < 0:
+            suggestions['risk_limit'] *= 0.9
+            suggestions['max_concurrent_trades'] = max(1, suggestions['max_concurrent_trades'] - 1)
+
+        # Market condition adjustments
+        if market_conditions.get('volatility', 0) > 0.03:
+            suggestions['min_profit_threshold'] *= 1.2
+            suggestions['correlation_threshold'] *= 0.9
+
+        if market_conditions.get('liquidity', 1) < 0.5:
+            suggestions['max_position_size'] *= 0.7
+
+        # Ensure parameters stay within reasonable bounds
+        suggestions = self._apply_parameter_bounds(suggestions)
+
+        # Store suggestions
+        self.parameter_history['suggestions'].append({
+            'timestamp': datetime.now(),
+            'parameters': suggestions,
+            'reasoning': self._generate_reasoning(recent_performance, market_conditions),
+            'performance_metrics': {
+                'success_rate': success_rate,
+                'avg_slippage': avg_slippage,
+                'avg_profit': avg_profit
+            }
+        })
+
+        self.current_parameters = suggestions
+        return suggestions
+
+    def _apply_parameter_bounds(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
+        """Apply bounds to parameters"""
+        bounds = {
+            'min_profit_threshold': (0.001, 0.01),
+            'max_position_size': (10000, 500000),
+            'risk_limit': (0.005, 0.05),
+            'correlation_threshold': (0.5, 0.9),
+            'rebalance_frequency': (60, 3600),
+            'latency_buffer': (1.0, 3.0),
+            'confidence_threshold': (0.5, 0.9),
+            'max_concurrent_trades': (1, 10)
+        }
+
+        bounded = parameters.copy()
+        for param, (min_val, max_val) in bounds.items():
+            if param in bounded:
+                bounded[param] = max(min_val, min(max_val, bounded[param]))
+
+        return bounded
+
+    def _generate_reasoning(self, performance: List[ExecutionResult],
+                          market_conditions: Dict[str, Any]) -> str:
+        """Generate reasoning for parameter adjustments"""
+
+        reasons = []
+
+        if performance:
+            success_rate = sum(1 for r in performance if r.success) / len(performance)
+            if success_rate < 0.7:
+                reasons.append("Low success rate detected - increasing selectivity")
+
+            avg_slippage = np.mean([r.slippage for r in performance])
+            if avg_slippage > 0.001:
+                reasons.append("High slippage observed - adjusting execution parameters")
+
+            avg_profit = np.mean([r.realized_profit for r in performance])
+            if avg_profit < 0:
+                reasons.append("Negative average profit - tightening risk controls")
+
+        if market_conditions.get('volatility', 0) > 0.03:
+            reasons.append("Elevated market volatility - implementing conservative measures")
+
+        if market_conditions.get('liquidity', 1) < 0.5:
+            reasons.append("Reduced liquidity conditions - scaling down position sizes")
+
+        return "; ".join(reasons) if reasons else "Standard market conditions"
+
+class RiskAnalytics:
+    """Comprehensive risk analytics system with advanced metrics"""
+
+    def __init__(self):
+        self.position_history = []
+        self.var_confidence = 0.95
+        self.risk_metrics_history = []
+        self.correlation_matrix = None
+
+    def calculate_var(self, returns: np.ndarray, confidence: float = 0.95) -> float:
+        """Calculate Value at Risk using historical simulation"""
+        if len(returns) < 20:
+            return 0.02  # Default 2% VaR
+
+        return np.percentile(returns, (1 - confidence) * 100)
+
+    def calculate_cvar(self, returns: np.ndarray, confidence: float = 0.95) -> float:
+        """Calculate Conditional Value at Risk (Expected Shortfall)"""
+        var = self.calculate_var(returns, confidence)
+        return returns[returns <= var].mean()
+
+    def calculate_sharpe_ratio(self, returns: np.ndarray) -> float:
+        """Calculate Sharpe ratio"""
+        if len(returns) < 2:
+            return 0.0
+
+        excess_returns = returns - RISK_FREE_RATE / TRADING_DAYS_PER_YEAR
+        return np.sqrt(TRADING_DAYS_PER_YEAR) * excess_returns.mean() / (returns.std() + 1e-8)
+
+    def calculate_sortino_ratio(self, returns: np.ndarray) -> float:
+        """Calculate Sortino ratio (downside deviation)"""
+        if len(returns) < 2:
+            return 0.0
+
+        excess_returns = returns - RISK_FREE_RATE / TRADING_DAYS_PER_YEAR
+        downside_returns = returns[returns < 0]
+
+        if len(downside_returns) == 0:
+            return float('inf')  # No downside risk
+
+        downside_std = np.std(downside_returns)
+        return np.sqrt(TRADING_DAYS_PER_YEAR) * excess_returns.mean() / (downside_std + 1e-8)
+
+    def calculate_max_drawdown(self, equity_curve: np.ndarray) -> float:
+        """Calculate maximum drawdown"""
+        peak = np.maximum.accumulate(equity_curve)
+        drawdown = (peak - equity_curve) / peak
+        return np.max(drawdown)
+
+    def calculate_calmar_ratio(self, returns: np.ndarray, equity_curve: np.ndarray) -> float:
+        """Calculate Calmar ratio (return / max drawdown)"""
+        max_dd = self.calculate_max_drawdown(equity_curve)
+        if max_dd == 0:
+            return float('inf')
+
+        annual_return = returns.mean() * TRADING_DAYS_PER_YEAR
+        return annual_return / max_dd
+
+    def analyze_position_risk(self, positions: List[Dict[str, Any]],
+                            market_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
+        """Analyze risk for current positions with comprehensive metrics"""
+
+        if not positions:
+            return self._empty_risk_metrics()
+
+        # Calculate position values and correlations
+        position_values = []
+        position_returns = []
+
+        for position in positions:
+            asset = position['asset']
+            size = position['size']
+
+            if asset in market_data:
+                price = market_data[asset]['close'].iloc[-1]
+                value = size * price
+                position_values.append(value)
+
+                returns = market_data[asset]['close'].pct_change().dropna()
+                position_returns.append(returns)
+
+        total_value = sum(position_values)
+
+        # Calculate portfolio metrics
+        if position_returns:
+            # Create weighted portfolio returns
+            weights = np.array(position_values) / total_value
+            portfolio_returns = np.zeros(len(position_returns[0]))
+
+            for i, (weight, returns) in enumerate(zip(weights, position_returns)):
+                portfolio_returns += weight * returns.values
+
+            # Calculate all risk metrics
+            var = self.calculate_var(portfolio_returns)
+            cvar = self.calculate_cvar(portfolio_returns)
+            sharpe = self.calculate_sharpe_ratio(portfolio_returns)
+            sortino = self.calculate_sortino_ratio(portfolio_returns)
+
+            # Build equity curve
+            equity_curve = (1 + portfolio_returns).cumprod()
+            max_dd = self.calculate_max_drawdown(equity_curve)
+            calmar = self.calculate_calmar_ratio(portfolio_returns, equity_curve)
+
+            # Calculate correlation matrix
+            returns_df = pd.DataFrame({
+                f'asset_{i}': returns.values
+                for i, returns in enumerate(position_returns)
+            })
+            correlation_matrix = returns_df.corr()
+            self.correlation_matrix = correlation_matrix
+
+            avg_correlation = correlation_matrix.values[np.triu_indices_from(
+                correlation_matrix.values, k=1)].mean()
+        else:
+            var = cvar = sharpe = sortino = max_dd = calmar = avg_correlation = 0
+
+        # Calculate additional risk metrics
+        herfindahl_index = sum((v/total_value)**2 for v in position_values) if total_value > 0 else 0
+
+        risk_metrics = {
+            'total_exposure': total_value,
+            'var_95': var,
+            'cvar_95': cvar,
+            'sharpe_ratio': sharpe,
+            'sortino_ratio': sortino,
+            'max_drawdown': max_dd,
+            'calmar_ratio': calmar,
+            'position_count': len(positions),
+            'avg_correlation': avg_correlation,
+            'concentration_risk': max(position_values) / total_value if total_value > 0 else 0,
+            'herfindahl_index': herfindahl_index,
+            'timestamp': datetime.now()
+        }
+
+        self.risk_metrics_history.append(risk_metrics)
+
+        return risk_metrics
+
+    def check_risk_limits(self, proposed_trade: ArbitrageOpportunity,
+                         current_positions: List[Dict[str, Any]],
+                         risk_parameters: Dict[str, Any]) -> Tuple[bool, str]:
+        """Check if proposed trade violates risk limits"""
+
+        # Check position limit
+        position_value = proposed_trade.max_size * proposed_trade.buy_price
+
+        if position_value > risk_parameters['max_position_size']:
+            return False, "Position size exceeds limit"
+
+        # Check total exposure
+        current_exposure = sum(p['size'] * p['entry_price'] for p in current_positions)
+
+        if current_exposure + position_value > risk_parameters['max_position_size'] * 5:
+            return False, "Total exposure limit exceeded"
+
+        # Check concurrent trades
+        if len(current_positions) >= risk_parameters['max_concurrent_trades']:
+            return False, "Maximum concurrent trades reached"
+
+        # Check correlation with existing positions
+        same_asset_positions = [p for p in current_positions if p['asset'] == proposed_trade.asset]
+        if same_asset_positions:
+            return False, "Already have position in this asset"
+
+        # Check risk/reward ratio
+        if proposed_trade.expected_profit_pct < risk_parameters['min_profit_threshold']:
+            return False, "Profit below minimum threshold"
+
+        # Check latency risk
+        if proposed_trade.latency_risk > risk_parameters.get('max_latency_risk', 0.7):
+            return False, "Latency risk too high"
+
+        return True, "Risk checks passed"
+
+    def _empty_risk_metrics(self) -> Dict[str, Any]:
+        """Return empty risk metrics"""
+        return {
+            'total_exposure': 0,
+            'var_95': 0,
+            'cvar_95': 0,
+            'sharpe_ratio': 0,
+            'sortino_ratio': 0,
+            'max_drawdown': 0,
+            'calmar_ratio': 0,
+            'position_count': 0,
+            'avg_correlation': 0,
+            'concentration_risk': 0,
+            'herfindahl_index': 0,
+            'timestamp': datetime.now()
+        }
+
+class LatencyAwareExecutionEngine:
+    """Execution engine with realistic latency simulation and smart routing"""
+
+    def __init__(self):
+        self.execution_history = []
+        self.latency_model = self._build_latency_model()
+        self.slippage_model = self._build_slippage_model()
+        self.execution_analytics = defaultdict(list)
+
+    def _build_latency_model(self) -> Dict[str, Dict[str, float]]:
+        """Build latency model for different exchange pairs"""
+        return {
+            'cex_cex': {'mean': 100, 'std': 20},      # CEX to CEX
+            'cex_dex': {'mean': 1500, 'std': 500},    # CEX to DEX
+            'dex_dex': {'mean': 2000, 'std': 800},    # DEX to DEX
+        }
+
+    def _build_slippage_model(self) -> Dict[str, float]:
+        """Build slippage model based on market conditions"""
+        return {
+            'low_volatility': 0.0005,    # 5 bps
+            'normal': 0.001,              # 10 bps
+            'high_volatility': 0.002,     # 20 bps
+            'extreme': 0.005              # 50 bps
+        }
+
+    def simulate_execution(self, opportunity: ArbitrageOpportunity,
+                          buy_exchange: ExchangeSimulator,
+                          sell_exchange: ExchangeSimulator,
+                          market_conditions: Dict[str, Any]) -> ExecutionResult:
+        """Simulate order execution with realistic latency and slippage"""
+
+        # Determine exchange types
+        exchange_pair = self._get_exchange_pair_type(
+            opportunity.buy_exchange,
+            opportunity.sell_exchange
+        )
+
+        # Simulate latency
+        latency_params = self.latency_model[exchange_pair]
+        total_latency = np.random.normal(
+            latency_params['mean'],
+            latency_params['std']
+        )
+        total_latency = max(0, total_latency)  # Ensure non-negative
+
+        # Determine market volatility regime
+        volatility_regime = self._get_volatility_regime(market_conditions)
+        base_slippage = self.slippage_model[volatility_regime]
+
+        # Calculate price movement during latency (correlated with volatility)
+        volatility = market_conditions.get('volatility', 0.02)
+        price_drift = np.random.normal(0, base_slippage * np.sqrt(total_latency / 1000) * (1 + volatility * 10))
+
+        # Simulate buy execution
+        buy_price_adjusted = opportunity.buy_price * (1 + price_drift)
+        buy_book = buy_exchange.generate_order_book(
+            opportunity.asset,
+            buy_price_adjusted,
+            market_conditions=market_conditions
+        )
+
+        buy_fill_price, buy_fill_size = buy_exchange.execute_order(
+            buy_book, 'buy', opportunity.max_size
+        )
+
+        # Simulate sell execution (with additional latency)
+        sell_latency = np.random.normal(50, 10)
+        price_drift_sell = np.random.normal(
+            0,
+            base_slippage * np.sqrt((total_latency + sell_latency) / 1000) * (1 + volatility * 10)
+        )
+
+        sell_price_adjusted = opportunity.sell_price * (1 - price_drift_sell)
+        sell_book = sell_exchange.generate_order_book(
+            opportunity.asset,
+            sell_price_adjusted,
+            market_conditions=market_conditions
+        )
+
+        sell_fill_price, sell_fill_size = sell_exchange.execute_order(
+            sell_book, 'sell', min(buy_fill_size, opportunity.max_size)
+        )
+
+        # Calculate realized profit
+        executed_size = min(buy_fill_size, sell_fill_size)
+
+        # Transaction costs
+        buy_cost = buy_exchange.transaction_cost * buy_fill_price * executed_size
+        sell_cost = sell_exchange.transaction_cost * sell_fill_price * executed_size
+
+        # Gas costs for DEX
+        gas_cost = 0
+        if 'dex' in opportunity.buy_exchange.lower():
+            gas_cost += GAS_COST_USD
+        if 'dex' in opportunity.sell_exchange.lower():
+            gas_cost += GAS_COST_USD
+
+        # Net profit calculation
+        gross_profit = (sell_fill_price - buy_fill_price) * executed_size
+        net_profit = gross_profit - buy_cost - sell_cost - gas_cost
+
+        # Calculate slippage
+        expected_profit = (opportunity.sell_price - opportunity.buy_price) * executed_size
+        slippage = (expected_profit - gross_profit) / expected_profit if expected_profit > 0 else 0
+
+        # Determine success based on profitability
+        success = net_profit > 0 and executed_size > 0
+
+        result = ExecutionResult(
+            opportunity_id=opportunity.opportunity_id,
+            success=success,
+            executed_size=executed_size,
+            buy_fill_price=buy_fill_price,
+            sell_fill_price=sell_fill_price,
+            realized_profit=net_profit,
+            slippage=slippage,
+            latency_ms=total_latency,
+            gas_cost=gas_cost,
+            timestamp=datetime.now()
+        )
+
+        self.execution_history.append(result)
+
+        # Track execution analytics
+        self.execution_analytics['asset'].append(opportunity.asset)
+        self.execution_analytics['exchange_pair'].append(exchange_pair)
+        self.execution_analytics['volatility_regime'].append(volatility_regime)
+
+        return result
+
+    def _get_exchange_pair_type(self, buy_exchange: str, sell_exchange: str) -> str:
+        """Determine exchange pair type"""
+        buy_is_dex = 'dex' in buy_exchange.lower() or buy_exchange in EXCHANGES['dex']
+        sell_is_dex = 'dex' in sell_exchange.lower() or sell_exchange in EXCHANGES['dex']
+
+        if buy_is_dex and sell_is_dex:
+            return 'dex_dex'
+        elif not buy_is_dex and not sell_is_dex:
+            return 'cex_cex'
+        else:
+            return 'cex_dex'
+
+    def _get_volatility_regime(self, market_conditions: Dict[str, Any]) -> str:
+        """Determine current volatility regime"""
+        volatility = market_conditions.get('volatility', 0.02)
+
+        if volatility < 0.015:
+            return 'low_volatility'
+        elif volatility < 0.03:
+            return 'normal'
+        elif volatility < 0.05:
+            return 'high_volatility'
+        else:
+            return 'extreme'
+
+    def optimize_execution_path(self, opportunities: List[ArbitrageOpportunity],
+                              current_positions: List[Dict[str, Any]],
+                              risk_parameters: Dict[str, Any]) -> List[ArbitrageOpportunity]:
+        """Optimize execution order considering dependencies and risk"""
+
+        if not opportunities:
+            return []
+
+        # Score opportunities based on multiple factors
+        scored_opportunities = []
+
+        for opp in opportunities:
+            # Multi-factor scoring
+            profit_score = opp.expected_profit_pct
+            latency_penalty = opp.latency_risk * 0.5
+            size_score = min(opp.max_size * opp.buy_price / risk_parameters['max_position_size'], 1.0)
+
+            # Add forecast confidence if available
+            forecast_confidence = 1 - opp.metadata.get('forecast_uncertainty', 0.5)
+
+            # Combined score
+            total_score = profit_score * (1 - latency_penalty) * size_score * forecast_confidence
+
+            scored_opportunities.append((total_score, opp))
+
+        # Sort by score (highest first)
+        scored_opportunities.sort(key=lambda x: x[0], reverse=True)
+
+        # Select top opportunities that don't violate risk limits
+        selected = []
+        simulated_positions = current_positions.copy()
+
+        for score, opp in scored_opportunities:
+            # Simulate adding this position
+            can_add, reason = self._can_add_opportunity(
+                opp, simulated_positions, risk_parameters
+            )
+
+            if can_add:
+                selected.append(opp)
+                simulated_positions.append({
+                    'asset': opp.asset,
+                    'size': opp.max_size,
+                    'entry_price': opp.buy_price
+                })
+
+                if len(selected) >= risk_parameters['max_concurrent_trades']:
+                    break
+
+        return selected
+
+    def _can_add_opportunity(self, opportunity: ArbitrageOpportunity,
+                           positions: List[Dict[str, Any]],
+                           risk_parameters: Dict[str, Any]) -> Tuple[bool, str]:
+        """Check if opportunity can be added to positions"""
+
+        # Check if already have position in asset
+        for pos in positions:
+            if pos['asset'] == opportunity.asset:
+                return False, "Already have position in asset"
+
+        # Check total exposure
+        current_exposure = sum(p['size'] * p['entry_price'] for p in positions)
+        new_exposure = opportunity.max_size * opportunity.buy_price
+
+        if current_exposure + new_exposure > risk_parameters['max_position_size'] * 5:
+            return False, "Would exceed total exposure limit"
+
+        return True, "OK"
+
+class CrossAssetArbitrageEngine:
+    """Main arbitrage engine coordinating all components"""
+
+    def __init__(self):
+        # Initialize components
+        self.price_forecaster = PriceForecastingEngine()
+        self.arbitrage_detector = ArbitrageDetector()
+        self.strategy_optimizer = LLMStrategyOptimizer()
+        self.risk_analytics = RiskAnalytics()
+        self.execution_engine = LatencyAwareExecutionEngine()
+
+        # Exchange simulators
+        self.exchanges = {}
+        for exchange in EXCHANGES['cex']:
+            self.exchanges[exchange] = ExchangeSimulator('cex')
+        for exchange in EXCHANGES['dex']:
+            self.exchanges[exchange] = ExchangeSimulator('dex')
+
+        # State management
+        self.active_positions = []
+        self.portfolio_value = 100000  # Starting capital
+        self.performance_history = []
+        self.market_data_cache = {}
+        self.forecasts_cache = {}
+
+    def generate_market_data(self, assets: List[str], days: int = 100) -> Dict[str, pd.DataFrame]:
+        """Generate realistic correlated market data for multiple assets"""
+        market_data = {}
+
+        # Generate correlation matrix for assets
+        n_assets = len(assets)
+        correlation_matrix = np.eye(n_assets)
+
+        # Add correlations between assets
+        for i in range(n_assets):
+            for j in range(i+1, n_assets):
+                # Crypto assets are more correlated
+                if (assets[i] in ASSET_CLASSES['crypto_spot'] and
+                    assets[j] in ASSET_CLASSES['crypto_spot']):
+                    corr = np.random.uniform(0.6, 0.9)
+                # FX pairs have moderate correlation
+                elif (assets[i] in ASSET_CLASSES['fx_pairs'] and
+                      assets[j] in ASSET_CLASSES['fx_pairs']):
+                    corr = np.random.uniform(0.3, 0.6)
+                # Different asset classes have low correlation
+                else:
+                    corr = np.random.uniform(-0.2, 0.3)
+
+                correlation_matrix[i, j] = corr
+                correlation_matrix[j, i] = corr
+
+        # Generate correlated returns
+        mean_returns = np.zeros(n_assets)
+        volatilities = []
+
+        for asset in assets:
+            if asset in ASSET_CLASSES['crypto_spot']:
+                volatilities.append(0.015)  # Higher volatility
+            elif asset in ASSET_CLASSES['fx_pairs']:
+                volatilities.append(0.005)  # Lower volatility
+            else:
+                volatilities.append(0.01)   # Medium volatility
+
+        cov_matrix = np.outer(volatilities, volatilities) * correlation_matrix
+
+        # Generate returns
+        returns = np.random.multivariate_normal(mean_returns, cov_matrix, days)
+
+        # Generate price data for each asset
+        for i, asset in enumerate(assets):
+            # Base price
+            if asset in ASSET_CLASSES['crypto_spot']:
+                base_price = {'BTC': 45000, 'ETH': 3000, 'SOL': 100}.get(asset, 50)
+            elif asset in ASSET_CLASSES['equity_etfs']:
+                base_price = {'SPY': 450, 'QQQ': 380}.get(asset, 100)
+            else:
+                base_price = 1.0  # FX pairs
+
+            # Generate prices from returns
+            prices = base_price * np.exp(np.cumsum(returns[:, i]))
+
+            # Generate OHLCV data
+            dates = pd.date_range(end=datetime.now(), periods=days, freq='H')
+
+            data = pd.DataFrame({
+                'open': prices * (1 + np.random.normal(0, 0.002, days)),
+                'high': prices * (1 + np.abs(np.random.normal(0, 0.005, days))),
+                'low': prices * (1 - np.abs(np.random.normal(0, 0.005, days))),
+                'close': prices,
+                'volume': np.random.lognormal(15, 0.5, days)
+            }, index=dates)
+
+            # Ensure OHLC consistency
+            data['high'] = data[['open', 'high', 'close']].max(axis=1)
+            data['low'] = data[['open', 'low', 'close']].min(axis=1)
+
+            market_data[asset] = data
+
+        self.market_data_cache = market_data
+        return market_data
+
+    def update_order_books(self, market_data: Dict[str, pd.DataFrame]) -> Dict[str, Dict[str, OrderBook]]:
+        """Generate current order books for all exchanges"""
+        order_books = defaultdict(dict)
+
+        # Get current market conditions
+        market_conditions = self.calculate_market_conditions(market_data)
+
+        for asset, data in market_data.items():
+            current_price = data['close'].iloc[-1]
+
+            # Generate order books for each exchange
+            for exchange_name, exchange in self.exchanges.items():
+                # Add price variation between exchanges
+                price_variation = np.random.normal(0, 0.0005)
+                adjusted_price = current_price * (1 + price_variation)
+
+                # Vary spread based on exchange type and market conditions
+                base_spread = 5 if exchange.exchange_type == 'cex' else 15
+                volatility_adjustment = 1 + market_conditions['volatility'] * 20
+                spread_bps = base_spread * volatility_adjustment
+
+                order_book = exchange.generate_order_book(
+                    asset, adjusted_price, spread_bps, market_conditions
+                )
+
+                order_books[exchange_name][asset] = order_book
+
+        return dict(order_books)
+
+    def calculate_market_conditions(self, market_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
+        """Calculate current market conditions"""
+
+        volatilities = []
+        volumes = []
+        spreads = []
+
+        for asset, data in market_data.items():
+            returns = data['close'].pct_change().dropna()
+
+            # Calculate volatility (annualized)
+            volatility = returns.iloc[-24:].std() * np.sqrt(365 * 24)
+            volatilities.append(volatility)
+
+            # Calculate average volume
+            avg_volume = data['volume'].iloc[-24:].mean()
+            volumes.append(avg_volume)
+
+            # Calculate spread proxy
+            spread = (data['high'] - data['low']).iloc[-24:].mean() / data['close'].iloc[-24:].mean()
+            spreads.append(spread)
+
+        return {
+            'volatility': np.mean(volatilities),
+            'max_volatility': np.max(volatilities),
+            'liquidity': np.mean(volumes) / 1e6,  # Normalize
+            'avg_spread': np.mean(spreads),
+            'timestamp': datetime.now()
+        }
+
+    def generate_price_forecasts(self, market_data: Dict[str, pd.DataFrame]) -> Dict[str, Dict[str, np.ndarray]]:
+        """Generate price forecasts for all assets"""
+        forecasts = {}
+
+        for asset, data in market_data.items():
+            forecast = self.price_forecaster.forecast_prices(asset, data)
+            forecasts[asset] = forecast
+
+        self.forecasts_cache = forecasts
+        return forecasts
+
+    def run_arbitrage_cycle(self) -> Dict[str, Any]:
+        """Run complete arbitrage detection and execution cycle"""
+
+        # Get current market data
+        if not self.market_data_cache:
+            assets = []
+            for asset_class, asset_list in ASSET_CLASSES.items():
+                assets.extend(asset_list[:2])  # Use first 2 from each class
+            self.market_data_cache = self.generate_market_data(assets)
+
+        market_data = self.market_data_cache
+
+        # Generate price forecasts
+        forecasts = self.generate_price_forecasts(market_data)
+
+        # Update order books
+        order_books = self.update_order_books(market_data)
+
+        # Calculate market conditions
+        market_conditions = self.calculate_market_conditions(market_data)
+
+        # Update strategy parameters based on recent performance
+        recent_executions = self.execution_engine.execution_history[-20:]
+        strategy_params = self.strategy_optimizer.generate_parameter_suggestions(
+            recent_executions, market_conditions
+        )
+
+        # Find arbitrage opportunities with forecast integration
+        transaction_costs = {
+            exchange: sim.transaction_cost
+            for exchange, sim in self.exchanges.items()
+        }
+
+        opportunities = self.arbitrage_detector.find_opportunities(
+            order_books, transaction_costs, forecasts
+        )
+
+        # Filter based on strategy parameters
+        filtered_opportunities = [
+            opp for opp in opportunities
+            if opp.expected_profit_pct >= strategy_params['min_profit_threshold']
+        ]
+
+        # Risk analysis
+        risk_metrics = self.risk_analytics.analyze_position_risk(
+            self.active_positions, market_data
+        )
+
+        # Optimize execution order
+        selected_opportunities = self.execution_engine.optimize_execution_path(
+            filtered_opportunities, self.active_positions, strategy_params
+        )
+
+        # Execute selected opportunities
+        execution_results = []
+
+        for opportunity in selected_opportunities:
+            # Final risk check
+            can_execute, reason = self.risk_analytics.check_risk_limits(
+                opportunity, self.active_positions, strategy_params
+            )
+
+            if can_execute:
+                # Execute trade
+                buy_exchange = self.exchanges[opportunity.buy_exchange]
+                sell_exchange = self.exchanges[opportunity.sell_exchange]
+
+                result = self.execution_engine.simulate_execution(
+                    opportunity, buy_exchange, sell_exchange, market_conditions
+                )
+
+                execution_results.append(result)
+
+                # Update positions if successful
+                if result.success:
+                    self.active_positions.append({
+                        'asset': opportunity.asset,
+                        'size': result.executed_size,
+                        'entry_price': result.buy_fill_price,
+                        'exit_price': result.sell_fill_price,
+                        'profit': result.realized_profit,
+                        'timestamp': result.timestamp
+                    })
+
+                    # Update portfolio value
+                    self.portfolio_value += result.realized_profit
+
+        # Clean up completed positions (for this simulation, all arb trades complete immediately)
+        self.active_positions = [p for p in self.active_positions
+                               if (datetime.now() - p['timestamp']).seconds < 300]
+
+        # Store performance metrics
+        cycle_summary = {
+            'timestamp': datetime.now(),
+            'opportunities_found': len(opportunities),
+            'opportunities_executed': len(execution_results),
+            'successful_executions': sum(1 for r in execution_results if r.success),
+            'total_profit': sum(r.realized_profit for r in execution_results),
+            'portfolio_value': self.portfolio_value,
+            'risk_metrics': risk_metrics,
+            'market_conditions': market_conditions,
+            'strategy_parameters': strategy_params
+        }
+
+        self.performance_history.append(cycle_summary)
+
+        return cycle_summary
+
+# Visualization functions
+def create_opportunity_network(opportunities: List[ArbitrageOpportunity]) -> go.Figure:
+    """Create network visualization of arbitrage opportunities"""
+
+    # Create graph
+    G = nx.Graph()
+
+    # Add nodes and edges
+    for opp in opportunities:
+        G.add_edge(
+            opp.buy_exchange,
+            opp.sell_exchange,
+            weight=opp.expected_profit_pct,
+            asset=opp.asset
+        )
+
+    if len(G.nodes()) == 0:
+        # Empty graph
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No arbitrage opportunities found",
+            xref="paper", yref="paper",
+            x=0.5, y=0.5, showarrow=False
+        )
+        return fig
+
+    # Calculate layout
+    pos = nx.spring_layout(G, k=2, iterations=50)
+
+    # Create edge trace
+    edge_trace = []
+    for edge in G.edges(data=True):
+        x0, y0 = pos[edge[0]]
+        x1, y1 = pos[edge[1]]
+
+        edge_trace.append(go.Scatter(
+            x=[x0, x1, None],
+            y=[y0, y1, None],
+            mode='lines',
+            line=dict(
+                width=edge[2]['weight'] * 100,
+                color='rgba(125,125,125,0.5)'
+            ),
+            hoverinfo='text',
+            text=f"{edge[2]['asset']}: {edge[2]['weight']*100:.2f}%"
+        ))
+
+    # Create node trace
+    node_trace = go.Scatter(
+        x=[pos[node][0] for node in G.nodes()],
+        y=[pos[node][1] for node in G.nodes()],
+        mode='markers+text',
+        text=[node for node in G.nodes()],
+        textposition="top center",
+        marker=dict(
+            size=20,
+            color=['red' if 'dex' in node.lower() else 'blue' for node in G.nodes()],
+            line=dict(color='darkgray', width=2)
+        ),
+        hoverinfo='text'
+    )
+
+    # Create figure
+    fig = go.Figure(data=edge_trace + [node_trace])
+
+    fig.update_layout(
+        title="Arbitrage Opportunity Network",
+        showlegend=False,
+        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+        height=500
+    )
+
+    return fig
+
+def create_performance_dashboard(performance_history: List[Dict[str, Any]]) -> go.Figure:
+    """Create comprehensive performance dashboard"""
+
+    if not performance_history:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No performance data available",
+            xref="paper", yref="paper",
+            x=0.5, y=0.5, showarrow=False
+        )
+        return fig
+
+    # Convert to DataFrame
+    perf_df = pd.DataFrame(performance_history)
+
+    # Create subplots
+    fig = make_subplots(
+        rows=3, cols=2,
+        subplot_titles=(
+            'Portfolio Value', 'Profit per Cycle',
+            'Success Rate', 'Risk Metrics',
+            'Opportunities vs Executions', 'Market Conditions'
+        ),
+        specs=[
+            [{"type": "scatter"}, {"type": "scatter"}],
+            [{"type": "scatter"}, {"type": "scatter"}],
+            [{"type": "bar"}, {"type": "scatter"}]
+        ],
+        vertical_spacing=0.1,
+        horizontal_spacing=0.1
+    )
+
+    # Portfolio value
+    fig.add_trace(
+        go.Scatter(
+            x=perf_df['timestamp'],
+            y=perf_df['portfolio_value'],
+            mode='lines',
+            name='Portfolio Value',
+            line=dict(color='blue', width=2)
+        ),
+        row=1, col=1
+    )
+
+    # Profit per cycle
+    fig.add_trace(
+        go.Scatter(
+            x=perf_df['timestamp'],
+            y=perf_df['total_profit'],
+            mode='lines+markers',
+            name='Profit',
+            line=dict(color='green')
+        ),
+        row=1, col=2
+    )
+
+    # Success rate
+    perf_df['success_rate'] = perf_df.apply(
+        lambda x: x['successful_executions'] / x['opportunities_executed'] if x['opportunities_executed'] > 0 else 0,
+        axis=1
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=perf_df['timestamp'],
+            y=perf_df['success_rate'],
+            mode='lines',
+            name='Success Rate',
+            line=dict(color='orange')
+        ),
+        row=2, col=1
+    )
+
+    # Risk metrics (Sharpe ratio)
+    sharpe_values = [m['sharpe_ratio'] for m in perf_df['risk_metrics']]
+    fig.add_trace(
+        go.Scatter(
+            x=perf_df['timestamp'],
+            y=sharpe_values,
+            mode='lines',
+            name='Sharpe Ratio',
+            line=dict(color='purple')
+        ),
+        row=2, col=2
+    )
+
+    # Opportunities vs Executions
+    fig.add_trace(
+        go.Bar(
+            x=perf_df['timestamp'],
+            y=perf_df['opportunities_found'],
+            name='Found',
+            marker_color='lightblue'
+        ),
+        row=3, col=1
+    )
+    fig.add_trace(
+        go.Bar(
+            x=perf_df['timestamp'],
+            y=perf_df['opportunities_executed'],
+            name='Executed',
+            marker_color='darkblue'
+        ),
+        row=3, col=1
+    )
+
+    # Market volatility
+    volatility_values = [m['volatility'] for m in perf_df['market_conditions']]
+    fig.add_trace(
+        go.Scatter(
+            x=perf_df['timestamp'],
+            y=volatility_values,
+            mode='lines',
+            name='Volatility',
+            line=dict(color='red')
+        ),
+        row=3, col=2
+    )
+
+    # Update layout
+    fig.update_layout(
+        height=1000,
+        showlegend=False,
+        title_text="Cross-Asset Arbitrage Performance Dashboard"
+    )
+
+    # Update axes
+    fig.update_xaxes(title_text="Time", row=3, col=1)
+    fig.update_xaxes(title_text="Time", row=3, col=2)
+    fig.update_yaxes(title_text="Value ($)", row=1, col=1)
+    fig.update_yaxes(title_text="Profit ($)", row=1, col=2)
+    fig.update_yaxes(title_text="Rate", row=2, col=1)
+    fig.update_yaxes(title_text="Sharpe", row=2, col=2)
+    fig.update_yaxes(title_text="Count", row=3, col=1)
+    fig.update_yaxes(title_text="Volatility", row=3, col=2)
+
+    return fig
+
+def create_execution_analysis(execution_history: List[ExecutionResult]) -> go.Figure:
+    """Create execution analysis visualization"""
+
+    if not execution_history:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No execution data available",
+            xref="paper", yref="paper",
+            x=0.5, y=0.5, showarrow=False
+        )
+        return fig
+
+    # Convert to DataFrame
+    exec_df = pd.DataFrame([
+        {
+            'timestamp': e.timestamp,
+            'profit': e.realized_profit,
+            'slippage': e.slippage,
+            'latency': e.latency_ms,
+            'success': e.success
+        }
+        for e in execution_history
+    ])
+
+    # Create subplots
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=(
+            'Profit Distribution', 'Slippage Analysis',
+            'Latency Distribution', 'Success Rate Over Time'
+        ),
+        specs=[
+            [{"type": "histogram"}, {"type": "scatter"}],
+            [{"type": "histogram"}, {"type": "scatter"}]
+        ]
+    )
+
+    # Profit distribution
+    fig.add_trace(
+        go.Histogram(
+            x=exec_df['profit'],
+            nbinsx=30,
+            name='Profit',
+            marker_color='green'
+        ),
+        row=1, col=1
+    )
+
+    # Slippage over time
+    fig.add_trace(
+        go.Scatter(
+            x=exec_df['timestamp'],
+            y=exec_df['slippage'] * 100,  # Convert to percentage
+            mode='markers',
+            name='Slippage',
+            marker=dict(
+                color=exec_df['success'].map({True: 'blue', False: 'red'}),
+                size=8
+            )
+        ),
+        row=1, col=2
+    )
+
+    # Latency distribution
+    fig.add_trace(
+        go.Histogram(
+            x=exec_df['latency'],
+            nbinsx=30,
+            name='Latency',
+            marker_color='orange'
+        ),
+        row=2, col=1
+    )
+
+    # Success rate over time (rolling)
+    exec_df['success_int'] = exec_df['success'].astype(int)
+    exec_df['success_rate_rolling'] = exec_df['success_int'].rolling(
+        window=20, min_periods=1
+    ).mean()
+
+    fig.add_trace(
+        go.Scatter(
+            x=exec_df['timestamp'],
+            y=exec_df['success_rate_rolling'],
+            mode='lines',
+            name='Success Rate',
+            line=dict(color='purple', width=2)
+        ),
+        row=2, col=2
+    )
+
+    # Update layout
+    fig.update_layout(
+        height=700,
+        showlegend=False,
+        title_text="Execution Analysis"
+    )
+
+    # Update axes
+    fig.update_xaxes(title_text="Profit ($)", row=1, col=1)
+    fig.update_xaxes(title_text="Time", row=1, col=2)
+    fig.update_xaxes(title_text="Latency (ms)", row=2, col=1)
+    fig.update_xaxes(title_text="Time", row=2, col=2)
+    fig.update_yaxes(title_text="Count", row=1, col=1)
+    fig.update_yaxes(title_text="Slippage (%)", row=1, col=2)
+    fig.update_yaxes(title_text="Count", row=2, col=1)
+    fig.update_yaxes(title_text="Success Rate", row=2, col=2)
+
+    return fig
+
+# Gradio Interface
+def create_gradio_interface():
+    """Create the main Gradio interface"""
+
+    # Initialize engine
+    engine = CrossAssetArbitrageEngine()
+
+    def run_arbitrage_simulation(n_cycles, initial_capital, min_profit_threshold):
+        """Run arbitrage simulation"""
+
+        # Reset engine
+        engine.portfolio_value = float(initial_capital)
+        engine.performance_history = []
+        engine.execution_engine.execution_history = []
+        engine.active_positions = []
+
+        # Update strategy parameters
+        engine.strategy_optimizer.current_parameters['min_profit_threshold'] = float(min_profit_threshold) / 100
+
+        # Generate initial market data
+        assets = []
+        for asset_class, asset_list in ASSET_CLASSES.items():
+            assets.extend(asset_list[:2])  # Use 2 assets from each class
+
+        engine.generate_market_data(assets, days=200)
+
+        # Run simulation cycles
+        cycle_summaries = []
+        for i in range(int(n_cycles)):
+            # Update market data (simulate price movement)
+            for asset, data in engine.market_data_cache.items():
+                # Add new price point
+                last_price = data['close'].iloc[-1]
+                new_return = np.random.normal(0.0001, 0.01)
+                new_price = last_price * (1 + new_return)
+
+                new_row = pd.DataFrame({
+                    'open': [new_price * (1 + np.random.normal(0, 0.002))],
+                    'high': [new_price * (1 + abs(np.random.normal(0, 0.005)))],
+                    'low': [new_price * (1 - abs(np.random.normal(0, 0.005)))],
+                    'close': [new_price],
+                    'volume': [np.random.lognormal(15, 0.5)]
+                }, index=[data.index[-1] + pd.Timedelta(hours=1)])
+
+                # Ensure OHLC consistency
+                new_row['high'] = new_row[['open', 'high', 'close']].max(axis=1)
+                new_row['low'] = new_row[['open', 'low', 'close']].min(axis=1)
+
+                engine.market_data_cache[asset] = pd.concat([data, new_row])
+
+                # Keep only recent data
+                engine.market_data_cache[asset] = engine.market_data_cache[asset].iloc[-200:]
+
+            # Run arbitrage cycle
+            summary = engine.run_arbitrage_cycle()
+            cycle_summaries.append(summary)
+
+        # Create visualizations
+        opportunity_network = create_opportunity_network(
+            engine.arbitrage_detector.opportunity_history[-50:]
+        )
+
+        performance_dashboard = create_performance_dashboard(
+            engine.performance_history
+        )
+
+        execution_analysis = create_execution_analysis(
+            engine.execution_engine.execution_history
+        )
+
+        # Calculate summary statistics
+        total_profit = sum(s['total_profit'] for s in engine.performance_history)
+        total_return = (engine.portfolio_value - initial_capital) / initial_capital
+
+        if len(engine.execution_engine.execution_history) > 0:
+            success_rate = sum(
+                1 for e in engine.execution_engine.execution_history if e.success
+            ) / len(engine.execution_engine.execution_history)
+            avg_latency = np.mean([
+                e.latency_ms for e in engine.execution_engine.execution_history
+            ])
+            avg_slippage = np.mean([
+                e.slippage for e in engine.execution_engine.execution_history
+            ])
+        else:
+            success_rate = avg_latency = avg_slippage = 0
+
+        # Get latest risk metrics
+        if engine.risk_analytics.risk_metrics_history:
+            latest_risk = engine.risk_analytics.risk_metrics_history[-1]
+            sharpe = latest_risk['sharpe_ratio']
+            var_95 = latest_risk['var_95']
+        else:
+            sharpe = var_95 = 0
+
+        summary_text = f"""
+        ### Simulation Summary
+
+        **Performance Metrics:**
+        - Total Profit: ${total_profit:,.2f}
+        - Total Return: {total_return*100:.2f}%
+        - Final Portfolio Value: ${engine.portfolio_value:,.2f}
+        - Sharpe Ratio: {sharpe:.2f}
+        - VaR (95%): {var_95*100:.2f}%
+
+        **Execution Statistics:**
+        - Total Opportunities Found: {sum(s['opportunities_found'] for s in engine.performance_history)}
+        - Total Executions: {len(engine.execution_engine.execution_history)}
+        - Success Rate: {success_rate*100:.1f}%
+        - Average Latency: {avg_latency:.0f}ms
+        - Average Slippage: {avg_slippage*100:.2f}%
+
+        **Active Positions:** {len(engine.active_positions)}
+        """
+
+        # Latest opportunities table
+        recent_opps = []
+        for opp in engine.arbitrage_detector.opportunity_history[-10:]:
+            recent_opps.append({
+                'Asset': opp.asset,
+                'Buy Exchange': opp.buy_exchange,
+                'Sell Exchange': opp.sell_exchange,
+                'Spread': f"{(opp.sell_price - opp.buy_price)/opp.buy_price*100:.2f}%",
+                'Expected Profit': f"${opp.expected_profit:.2f}",
+                'Latency Risk': f"{opp.latency_risk:.2f}"
+            })
+
+        opportunities_df = pd.DataFrame(recent_opps) if recent_opps else pd.DataFrame()
+
+        return (opportunity_network, performance_dashboard, execution_analysis,
+                summary_text, opportunities_df)
+
+    def analyze_strategy_parameters():
+        """Analyze current strategy parameters"""
+
+        if not engine.strategy_optimizer.parameter_history:
+            return "No parameter history available", ""
+
+        # Get parameter evolution
+        param_history = engine.strategy_optimizer.parameter_history.get('suggestions', [])
+
+        if not param_history:
+            return "No parameter suggestions generated", ""
+
+        # Create parameter evolution chart
+        param_df = pd.DataFrame([
+            {
+                'timestamp': entry['timestamp'],
+                **entry['parameters']
+            }
+            for entry in param_history
+        ])
+
+        fig = make_subplots(
+            rows=2, cols=2,
+            subplot_titles=(
+                'Profit Threshold', 'Position Size',
+                'Risk Limit', 'Confidence Threshold'
+            )
+        )
+
+        # Plot each parameter
+        params_to_plot = [
+            ('min_profit_threshold', 1, 1, 'Threshold'),
+            ('max_position_size', 1, 2, 'Size ($)'),
+            ('risk_limit', 2, 1, 'Limit'),
+            ('confidence_threshold', 2, 2, 'Threshold')
+        ]
+
+        for param, row, col, ylabel in params_to_plot:
+            if param in param_df.columns:
+                fig.add_trace(
+                    go.Scatter(
+                        x=param_df['timestamp'],
+                        y=param_df[param],
+                        mode='lines+markers',
+                        name=param
+                    ),
+                    row=row, col=col
+                )
+
+        fig.update_layout(
+            height=600,
+            showlegend=False,
+            title_text="Strategy Parameter Evolution"
+        )
+
+        # Get latest reasoning
+        latest_reasoning = param_history[-1]['reasoning'] if param_history else "No reasoning available"
+
+        return fig, f"**Latest Optimization Reasoning:** {latest_reasoning}"
+
+    # Create interface
+    with gr.Blocks(title="Cross-Asset Arbitrage Engine") as interface:
+        gr.Markdown("""
+        # Cross-Asset Arbitrage Engine with Transformer Models
+
+        This sophisticated arbitrage engine leverages transformer models for price forecasting across multiple asset classes:
+        - **Crypto Spot/Futures**: BTC, ETH, SOL and perpetual futures
+        - **Foreign Exchange**: Major currency pairs
+        - **Equity ETFs**: SPY, QQQ, IWM and international markets
+
+        Features:
+        - **Transformer Price Prediction**: Numerical-adapted transformers for multi-horizon forecasting
+        - **Cross-Venue Execution**: Simulates CEX (Binance, Coinbase) and DEX (Uniswap V3) integration
+        - **LLM Strategy Optimization**: Dynamic parameter adjustment based on performance
+        - **Latency-Aware Execution**: Realistic order routing with slippage simulation
+        - **Comprehensive Risk Analytics**: Real-time VaR, Sharpe ratio, and drawdown monitoring
+
+        Author: Spencer Purdy
+        """)
+
+        with gr.Tab("Arbitrage Simulation"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    n_cycles = gr.Slider(
+                        minimum=10, maximum=100, value=50, step=10,
+                        label="Number of Trading Cycles"
+                    )
+                    initial_capital = gr.Number(
+                        value=100000, label="Initial Capital ($)", minimum=10000
+                    )
+                    min_profit = gr.Slider(
+                        minimum=0.1, maximum=1.0, value=0.2, step=0.1,
+                        label="Minimum Profit Threshold (%)"
+                    )
+
+                    run_btn = gr.Button("Run Simulation", variant="primary")
+
+            with gr.Row():
+                opportunity_network = gr.Plot(label="Arbitrage Opportunity Network")
+
+            with gr.Row():
+                performance_dashboard = gr.Plot(label="Performance Dashboard")
+
+            with gr.Row():
+                execution_analysis = gr.Plot(label="Execution Analysis")
+
+            with gr.Row():
+                with gr.Column(scale=1):
+                    summary_display = gr.Markdown(label="Summary Statistics")
+
+                with gr.Column(scale=1):
+                    opportunities_table = gr.DataFrame(
+                        label="Recent Arbitrage Opportunities"
+                    )
+
+        with gr.Tab("Strategy Analysis"):
+            with gr.Row():
+                analyze_btn = gr.Button("Analyze Strategy Parameters", variant="primary")
+
+            with gr.Row():
+                param_evolution = gr.Plot(label="Parameter Evolution")
+
+            with gr.Row():
+                param_reasoning = gr.Markdown(label="Optimization Reasoning")
+
+        # Event handlers
+        run_btn.click(
+            fn=run_arbitrage_simulation,
+            inputs=[n_cycles, initial_capital, min_profit],
+            outputs=[
+                opportunity_network, performance_dashboard,
+                execution_analysis, summary_display, opportunities_table
+            ]
+        )
+
+        analyze_btn.click(
+            fn=analyze_strategy_parameters,
+            inputs=[],
+            outputs=[param_evolution, param_reasoning]
+        )
+
+        # Add examples
+        gr.Examples(
+            examples=[
+                [50, 100000, 0.2],
+                [30, 50000, 0.3],
+                [100, 200000, 0.15]
+            ],
+            inputs=[n_cycles, initial_capital, min_profit]
+        )
+
+    return interface
+
+# Launch application
+if __name__ == "__main__":
+    interface = create_gradio_interface()
+    interface.launch()