Financial Tools

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from aimatrix.tools.financial import StripeTool

# Initialize Stripe integration
stripe = StripeTool(
    api_key="sk_live_...",  # Use environment variables in production
    webhook_secret="whsec_...",
    auto_tax_calculation=True,
    fraud_detection=True
)

# AI agent can handle complex payment scenarios
payment_agent = AIAgent(
    name="payment-processor",
    tools=[stripe],
    instructions="""
    You are a payment processing expert. You can:
    1. Process payments with fraud detection
    2. Handle payment failures and retries
    3. Manage subscriptions and recurring billing
    4. Process refunds and chargebacks
    5. Generate financial reports
    """
)

# Example: Intelligent subscription management
@payment_agent.task("manage_subscription")
async def handle_subscription_changes(customer_id: str, action: str, details: dict):
    """Handle subscription changes with smart business logic"""
    
    customer = await stripe.get_customer(customer_id)
    
    if action == "upgrade":
        # Calculate prorated amount
        proration = await stripe.calculate_proration(
            customer_id=customer_id,
            new_plan=details["new_plan"],
            billing_cycle_anchor="now"
        )
        
        # Apply upgrade with appropriate billing
        subscription = await stripe.modify_subscription(
            subscription_id=customer.subscription_id,
            items=[{
                "id": customer.subscription_items[0].id,
                "plan": details["new_plan"]
            }],
            proration_behavior="always_invoice"
        )
        
        # Send upgrade confirmation
        await send_notification(
            customer_id=customer_id,
            type="subscription_upgraded",
            data={
                "new_plan": details["new_plan"],
                "prorated_amount": proration.amount,
                "next_billing_date": subscription.current_period_end
            }
        )
        
    elif action == "cancel":
        # Smart cancellation with retention offer
        retention_offer = await generate_retention_offer(customer)
        
        if retention_offer and not details.get("immediate_cancel"):
            # Offer retention deal
            await send_retention_offer(customer_id, retention_offer)
            
            # Schedule follow-up if no response
            await schedule_task(
                task="follow_up_cancellation",
                delay_days=3,
                params={"customer_id": customer_id}
            )
        else:
            # Process immediate cancellation
            await stripe.cancel_subscription(
                subscription_id=customer.subscription_id,
                at_period_end=details.get("at_period_end", True)
            )
            
            # Send cancellation confirmation
            await send_notification(
                customer_id=customer_id,
                type="subscription_cancelled",
                data={
                    "cancellation_date": subscription.canceled_at,
                    "access_until": subscription.current_period_end
                }
            )

# Advanced payment processing with AI
async def process_intelligent_payment(payment_data: dict):
    """Process payment with AI-powered fraud detection and optimization"""
    
    # Pre-process payment with AI risk assessment
    risk_assessment = await stripe.analyze_payment_risk(payment_data)
    
    if risk_assessment.risk_level == "high":
        # Request additional verification
        verification = await stripe.request_3d_secure(
            payment_method=payment_data["payment_method_id"],
            amount=payment_data["amount"],
            currency=payment_data["currency"]
        )
        
        if not verification.authenticated:
            return {"status": "failed", "reason": "authentication_failed"}
    
    # Optimize payment routing
    optimal_processor = await stripe.select_optimal_processor(
        amount=payment_data["amount"],
        currency=payment_data["currency"],
        customer_location=payment_data["billing_address"]["country"]
    )
    
    # Process payment
    try:
        payment_intent = await stripe.create_payment_intent(
            amount=payment_data["amount"],
            currency=payment_data["currency"],
            payment_method=payment_data["payment_method_id"],
            customer=payment_data["customer_id"],
            metadata={
                "risk_score": risk_assessment.score,
                "processor": optimal_processor.name
            }
        )
        
        return {"status": "succeeded", "payment_intent": payment_intent}
        
    except PaymentError as e:
        # Intelligent retry logic
        if e.decline_code in ["insufficient_funds", "temporary_hold"]:
            # Schedule retry for later
            await schedule_payment_retry(
                payment_data=payment_data,
                retry_after_hours=24
            )
            return {"status": "retry_scheduled", "reason": str(e)}
        else:
            # Permanent failure
            return {"status": "failed", "reason": str(e)}

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from aimatrix.tools.financial import PayPalTool

# Initialize PayPal integration
paypal = PayPalTool(
    client_id="your_client_id",
    client_secret="your_client_secret",
    environment="production",  # or "sandbox"
    webhook_verification=True
)

# Handle PayPal payments and payouts
@paypal.webhook_handler("PAYMENT.CAPTURE.COMPLETED")
async def handle_payment_completed(webhook_event):
    """Handle completed PayPal payments"""
    
    payment_data = webhook_event["resource"]
    
    # Update internal records
    await update_payment_status(
        payment_id=payment_data["id"],
        status="completed",
        amount=payment_data["amount"]["value"],
        currency=payment_data["amount"]["currency_code"]
    )
    
    # Send confirmation to customer
    await send_payment_confirmation(
        customer_email=payment_data["payer"]["email_address"],
        payment_id=payment_data["id"],
        amount=payment_data["amount"]["value"]
    )

# Marketplace payments with PayPal
async def process_marketplace_payment(transaction_data: dict):
    """Handle complex marketplace payments with multiple recipients"""
    
    # Create payment with multiple recipients
    payout = await paypal.create_batch_payout(
        sender_batch_header={
            "sender_batch_id": f"batch_{transaction_data['order_id']}",
            "email_subject": "Payment from Marketplace",
            "email_message": "You have received a payment"
        },
        items=[
            {
                "recipient_type": "EMAIL",
                "amount": {
                    "value": str(transaction_data["seller_amount"]),
                    "currency": transaction_data["currency"]
                },
                "receiver": transaction_data["seller_email"],
                "note": f"Payment for order {transaction_data['order_id']}"
            },
            {
                "recipient_type": "EMAIL", 
                "amount": {
                    "value": str(transaction_data["affiliate_commission"]),
                    "currency": transaction_data["currency"]
                },
                "receiver": transaction_data["affiliate_email"],
                "note": f"Commission for order {transaction_data['order_id']}"
            }
        ]
    )
    
    return payout

# PayPal subscription management
async def manage_paypal_subscription(customer_id: str, plan_id: str):
    """Create and manage PayPal subscriptions"""
    
    # Create subscription
    subscription = await paypal.create_subscription(
        plan_id=plan_id,
        subscriber={
            "email_address": await get_customer_email(customer_id),
            "name": {
                "given_name": await get_customer_first_name(customer_id),
                "surname": await get_customer_last_name(customer_id)
            }
        },
        application_context={
            "brand_name": "Your Company",
            "locale": "en-US",
            "shipping_preference": "NO_SHIPPING",
            "user_action": "SUBSCRIBE_NOW",
            "payment_method": {
                "payer_selected": "PAYPAL",
                "payee_preferred": "IMMEDIATE_PAYMENT_REQUIRED"
            },
            "return_url": "https://your-site.com/subscription/success",
            "cancel_url": "https://your-site.com/subscription/cancel"
        }
    )
    
    return subscription

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from aimatrix.tools.financial import PaymentOrchestrator

# Initialize payment orchestrator
payment_orchestrator = PaymentOrchestrator(
    providers={
        "stripe": StripeTool(api_key="sk_..."),
        "paypal": PayPalTool(client_id="...", client_secret="..."),
        "square": SquareTool(access_token="..."),
        "adyen": AdyenTool(api_key="...", merchant_account="...")
    },
    routing_rules={
        "optimize_for": "success_rate",  # or "cost", "speed"
        "fallback_enabled": True,
        "region_preferences": {
            "US": ["stripe", "paypal", "square"],
            "EU": ["stripe", "adyen", "paypal"],
            "ASIA": ["stripe", "local_providers"]
        }
    }
)

# Intelligent payment processing
async def process_optimized_payment(payment_data: dict):
    """Process payment using optimal provider selection"""
    
    # AI selects best payment provider
    optimal_provider = await payment_orchestrator.select_provider(
        amount=payment_data["amount"],
        currency=payment_data["currency"],
        customer_location=payment_data["customer_location"],
        payment_method=payment_data["payment_method_type"],
        historical_success_rate=True
    )
    
    # Process payment with primary provider
    try:
        result = await optimal_provider.process_payment(payment_data)
        
        if result.status == "succeeded":
            return result
            
    except PaymentProviderError as e:
        # Auto-failover to secondary provider
        logger.warning(f"Payment failed with {optimal_provider.name}: {str(e)}")
        
        fallback_provider = await payment_orchestrator.get_fallback_provider(
            original_provider=optimal_provider,
            payment_data=payment_data
        )
        
        if fallback_provider:
            try:
                result = await fallback_provider.process_payment(payment_data)
                return result
            except Exception as fallback_error:
                logger.error(f"Fallback payment also failed: {str(fallback_error)}")
                raise
    
    raise PaymentProcessingError("All payment providers failed")

# Real-time payment analytics
@payment_orchestrator.analytics
async def track_payment_metrics():
    """Track payment performance across all providers"""
    
    metrics = await payment_orchestrator.get_metrics(
        timeframe="last_24_hours",
        include=["success_rate", "average_processing_time", "cost_per_transaction"]
    )
    
    # Generate performance report
    report = {
        "total_transactions": metrics.total_count,
        "success_rate": metrics.overall_success_rate,
        "provider_performance": {
            provider: {
                "success_rate": metrics.providers[provider].success_rate,
                "avg_time": metrics.providers[provider].avg_processing_time,
                "cost_per_txn": metrics.providers[provider].avg_cost
            }
            for provider in metrics.providers
        },
        "recommendations": await payment_orchestrator.generate_optimization_recommendations()
    }
    
    return report

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from aimatrix.tools.financial import OpenBankingTool

# Initialize Open Banking client
open_banking = OpenBankingTool(
    region="EU",  # or "UK", "AU", etc.
    psd2_compliant=True,
    tpp_id="your_tpp_id",
    certificates={
        "signing_cert": "path/to/signing.pem",
        "transport_cert": "path/to/transport.pem"
    }
)

# Account Information Service (AIS)
async def get_customer_financial_overview(customer_id: str):
    """Get comprehensive financial overview using Open Banking"""
    
    # Get customer's consented bank accounts
    accounts = await open_banking.get_accounts(customer_id)
    
    financial_overview = {
        "total_balance": 0,
        "accounts": [],
        "monthly_income": 0,
        "monthly_expenses": 0,
        "savings_rate": 0
    }
    
    for account in accounts:
        # Get account balance
        balance = await open_banking.get_balance(account.id)
        
        # Get recent transactions for analysis
        transactions = await open_banking.get_transactions(
            account_id=account.id,
            from_date=(datetime.now() - timedelta(days=90)).isoformat(),
            to_date=datetime.now().isoformat()
        )
        
        # AI-powered transaction categorization
        categorized_transactions = await categorize_transactions(transactions)
        
        account_summary = {
            "account_id": account.id,
            "bank_name": account.servicer_name,
            "account_type": account.cash_account_type,
            "balance": balance.amount,
            "currency": balance.currency,
            "monthly_income": sum(t.amount for t in categorized_transactions if t.category == "income"),
            "monthly_expenses": sum(abs(t.amount) for t in categorized_transactions if t.amount < 0),
            "top_merchants": get_top_merchants(categorized_transactions)
        }
        
        financial_overview["accounts"].append(account_summary)
        financial_overview["total_balance"] += balance.amount
        financial_overview["monthly_income"] += account_summary["monthly_income"]
        financial_overview["monthly_expenses"] += account_summary["monthly_expenses"]
    
    # Calculate savings rate
    if financial_overview["monthly_income"] > 0:
        financial_overview["savings_rate"] = (
            (financial_overview["monthly_income"] - financial_overview["monthly_expenses"]) 
            / financial_overview["monthly_income"] * 100
        )
    
    return financial_overview

# Payment Initiation Service (PIS)
async def initiate_smart_payment(payment_request: dict):
    """Initiate payment with optimal bank selection"""
    
    # Get customer's available accounts
    accounts = await open_banking.get_accounts(payment_request["customer_id"])
    
    # Select optimal account for payment
    optimal_account = await select_optimal_account(
        accounts=accounts,
        amount=payment_request["amount"],
        criteria=["sufficient_balance", "lowest_fees", "fastest_processing"]
    )
    
    # Initiate payment
    payment = await open_banking.initiate_payment(
        debtor_account=optimal_account.id,
        creditor_account=payment_request["recipient_account"],
        amount=payment_request["amount"],
        currency=payment_request["currency"],
        reference=payment_request["reference"]
    )
    
    # Monitor payment status
    await monitor_payment_status(payment.payment_id)
    
    return payment

# AI-powered financial advice
async def generate_financial_insights(customer_id: str):
    """Generate personalized financial insights and recommendations"""
    
    # Get financial data
    overview = await get_customer_financial_overview(customer_id)
    
    # AI analysis
    insights = await ai_agent.analyze_financial_data(overview)
    
    recommendations = []
    
    # Savings recommendations
    if overview["savings_rate"] < 20:
        recommendations.append({
            "type": "savings",
            "message": f"Consider increasing your savings rate to 20%. You're currently saving {overview['savings_rate']:.1f}%",
            "action": "Set up automatic transfer to savings account",
            "potential_impact": "Build emergency fund and long-term wealth"
        })
    
    # Spending optimization
    high_spending_categories = insights.get_high_spending_categories()
    for category in high_spending_categories:
        if category["percentage_of_income"] > 30:
            recommendations.append({
                "type": "spending_optimization",
                "message": f"High spending on {category['name']}: {category['percentage_of_income']:.1f}% of income",
                "action": f"Review and optimize {category['name']} expenses",
                "potential_savings": category["optimization_potential"]
            })
    
    # Account optimization
    if len(overview["accounts"]) > 1:
        account_analysis = await analyze_account_optimization(overview["accounts"])
        if account_analysis.consolidation_benefit > 50:  # €50 monthly savings
            recommendations.append({
                "type": "account_optimization",
                "message": "Consider consolidating accounts to reduce fees",
                "action": "Close underutilized accounts",
                "potential_savings": f"€{account_analysis.consolidation_benefit}/month"
            })
    
    return {
        "overview": overview,
        "insights": insights,
        "recommendations": recommendations
    }

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from aimatrix.tools.financial import PlaidTool

# Initialize Plaid integration
plaid = PlaidTool(
    client_id="your_client_id",
    secret="your_secret",
    environment="production",  # or "sandbox", "development"
    products=["transactions", "auth", "identity", "assets", "investments"]
)

# Account linking and verification
async def link_bank_account(customer_id: str, public_token: str):
    """Link and verify customer bank account"""
    
    # Exchange public token for access token
    exchange_response = await plaid.exchange_public_token(public_token)
    access_token = exchange_response["access_token"]
    
    # Store encrypted access token
    await store_encrypted_token(customer_id, access_token)
    
    # Get account information
    accounts = await plaid.get_accounts(access_token)
    
    # Verify account ownership
    identity = await plaid.get_identity(access_token)
    
    # Enhanced account verification
    verification_result = await plaid.get_auth(access_token)
    
    account_data = {
        "customer_id": customer_id,
        "accounts": [
            {
                "account_id": account.account_id,
                "name": account.name,
                "type": account.type,
                "subtype": account.subtype,
                "mask": account.mask,
                "balance": account.balances.current,
                "verified": account.account_id in [v.account_id for v in verification_result.accounts]
            }
            for account in accounts.accounts
        ],
        "identity_verified": len(identity.accounts) > 0
    }
    
    return account_data

# Advanced transaction analysis
async def analyze_financial_behavior(customer_id: str):
    """Comprehensive financial behavior analysis"""
    
    access_token = await get_encrypted_token(customer_id)
    
    # Get transactions for the last year
    end_date = datetime.now()
    start_date = end_date - timedelta(days=365)
    
    transactions = await plaid.get_transactions(
        access_token=access_token,
        start_date=start_date.date(),
        end_date=end_date.date()
    )
    
    # AI-powered transaction analysis
    analysis = {
        "spending_patterns": {},
        "income_stability": {},
        "cash_flow_analysis": {},
        "financial_health_score": 0
    }
    
    # Categorize and analyze transactions
    monthly_data = {}
    for transaction in transactions.transactions:
        month_key = transaction.date.strftime("%Y-%m")
        
        if month_key not in monthly_data:
            monthly_data[month_key] = {
                "income": 0,
                "expenses": 0,
                "categories": {}
            }
        
        # Income vs expenses
        if transaction.amount < 0:  # Plaid shows credits as negative
            monthly_data[month_key]["income"] += abs(transaction.amount)
        else:
            monthly_data[month_key]["expenses"] += transaction.amount
            
            # Categorize expenses
            category = transaction.category[0] if transaction.category else "Other"
            if category not in monthly_data[month_key]["categories"]:
                monthly_data[month_key]["categories"][category] = 0
            monthly_data[month_key]["categories"][category] += transaction.amount
    
    # Calculate financial health metrics
    monthly_incomes = [data["income"] for data in monthly_data.values()]
    monthly_expenses = [data["expenses"] for data in monthly_data.values()]
    
    analysis["income_stability"] = {
        "average_income": statistics.mean(monthly_incomes),
        "income_volatility": statistics.stdev(monthly_incomes) if len(monthly_incomes) > 1 else 0,
        "income_trend": calculate_trend(monthly_incomes)
    }
    
    analysis["spending_patterns"] = {
        "average_expenses": statistics.mean(monthly_expenses),
        "expense_volatility": statistics.stdev(monthly_expenses) if len(monthly_expenses) > 1 else 0,
        "top_categories": get_top_spending_categories(monthly_data)
    }
    
    analysis["cash_flow_analysis"] = {
        "average_monthly_surplus": statistics.mean([
            monthly_data[month]["income"] - monthly_data[month]["expenses"]
            for month in monthly_data
        ]),
        "months_with_deficit": len([
            month for month in monthly_data
            if monthly_data[month]["income"] < monthly_data[month]["expenses"]
        ])
    }
    
    # Calculate financial health score (0-100)
    health_score = calculate_financial_health_score(analysis)
    analysis["financial_health_score"] = health_score
    
    return analysis

# Investment tracking
async def track_investment_portfolio(customer_id: str):
    """Track investment accounts and performance"""
    
    access_token = await get_encrypted_token(customer_id)
    
    # Get investment accounts
    investments = await plaid.get_investments(access_token)
    
    portfolio_analysis = {
        "total_value": 0,
        "accounts": [],
        "asset_allocation": {},
        "performance_metrics": {}
    }
    
    for account in investments.accounts:
        if account.type == "investment":
            # Get holdings for this account
            holdings = [h for h in investments.holdings if h.account_id == account.account_id]
            
            account_value = sum(holding.institution_value for holding in holdings)
            portfolio_analysis["total_value"] += account_value
            
            account_data = {
                "account_id": account.account_id,
                "name": account.name,
                "value": account_value,
                "holdings": [
                    {
                        "security_id": holding.security_id,
                        "quantity": holding.quantity,
                        "value": holding.institution_value,
                        "cost_basis": holding.cost_basis
                    }
                    for holding in holdings
                ]
            }
            
            portfolio_analysis["accounts"].append(account_data)
            
            # Asset allocation
            for holding in holdings:
                security = next(
                    (s for s in investments.securities if s.security_id == holding.security_id),
                    None
                )
                if security:
                    asset_type = security.type or "Other"
                    if asset_type not in portfolio_analysis["asset_allocation"]:
                        portfolio_analysis["asset_allocation"][asset_type] = 0
                    portfolio_analysis["asset_allocation"][asset_type] += holding.institution_value
    
    # Calculate allocation percentages
    if portfolio_analysis["total_value"] > 0:
        for asset_type in portfolio_analysis["asset_allocation"]:
            portfolio_analysis["asset_allocation"][asset_type] = (
                portfolio_analysis["asset_allocation"][asset_type] / 
                portfolio_analysis["total_value"] * 100
            )
    
    return portfolio_analysis

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from aimatrix.tools.financial import CryptocurrencyTool

# Initialize cryptocurrency tools
crypto = CryptocurrencyTool(
    networks=["bitcoin", "ethereum", "polygon", "binance_smart_chain"],
    wallet_management=True,
    defi_protocols=True,
    security_level="enterprise"
)

# Multi-chain wallet management
async def create_enterprise_wallet(customer_id: str, currencies: list):
    """Create secure multi-currency wallet"""
    
    # Generate secure wallet with hardware security module
    wallet = await crypto.create_wallet(
        customer_id=customer_id,
        currencies=currencies,
        security_features={
            "multi_signature": True,
            "hardware_backup": True,
            "social_recovery": True,
            "daily_limits": True
        }
    )
    
    # Set up monitoring
    await crypto.setup_wallet_monitoring(
        wallet_id=wallet.id,
        alerts={
            "large_transactions": {"threshold": 10000, "currency": "USD"},
            "unusual_activity": True,
            "balance_changes": True,
            "security_events": True
        }
    )
    
    return wallet

# Automated cryptocurrency trading
async def execute_crypto_strategy(strategy_id: str, parameters: dict):
    """Execute automated crypto trading strategy"""
    
    strategy = await get_trading_strategy(strategy_id)
    
    # Market analysis
    market_data = await crypto.get_market_data(
        symbols=strategy.symbols,
        timeframe="1h",
        indicators=["RSI", "MACD", "BB", "EMA"]
    )
    
    # AI-powered trading decisions
    trading_signals = await ai_agent.analyze_crypto_market(
        market_data=market_data,
        strategy=strategy,
        parameters=parameters
    )
    
    executed_trades = []
    
    for signal in trading_signals:
        if signal.confidence > parameters.get("min_confidence", 0.7):
            try:
                # Execute trade with slippage protection
                trade = await crypto.execute_trade(
                    symbol=signal.symbol,
                    side=signal.side,  # "buy" or "sell"
                    quantity=signal.quantity,
                    order_type="limit",
                    price=signal.price,
                    slippage_tolerance=parameters.get("slippage_tolerance", 0.5)
                )
                
                executed_trades.append(trade)
                
                # Risk management
                if signal.side == "buy":
                    # Set stop-loss and take-profit orders
                    await crypto.create_conditional_order(
                        symbol=signal.symbol,
                        side="sell",
                        quantity=signal.quantity,
                        trigger_type="stop_loss",
                        trigger_price=signal.price * (1 - parameters.get("stop_loss", 0.05))
                    )
                    
                    await crypto.create_conditional_order(
                        symbol=signal.symbol,
                        side="sell",
                        quantity=signal.quantity * parameters.get("take_profit_ratio", 0.5),
                        trigger_type="take_profit",
                        trigger_price=signal.price * (1 + parameters.get("take_profit", 0.10))
                    )
                    
            except TradingError as e:
                logger.error(f"Trade execution failed for {signal.symbol}: {str(e)}")
                
                # Notify risk management
                await send_alert(
                    type="trading_error",
                    message=f"Failed to execute {signal.side} order for {signal.symbol}: {str(e)}"
                )
    
    return executed_trades

# DeFi protocol integration
async def manage_defi_portfolio(wallet_id: str, strategy: str):
    """Manage DeFi investments across multiple protocols"""
    
    wallet = await crypto.get_wallet(wallet_id)
    
    if strategy == "yield_farming":
        # Find optimal yield farming opportunities
        opportunities = await crypto.scan_yield_opportunities(
            protocols=["uniswap", "compound", "aave", "yearn"],
            min_apy=5.0,
            risk_level="medium"
        )
        
        # Sort by risk-adjusted returns
        optimal_opportunities = sorted(
            opportunities,
            key=lambda x: x.apy / (1 + x.risk_score),
            reverse=True
        )
        
        # Diversify across top opportunities
        total_investment = wallet.get_balance("USDC")
        allocation_per_protocol = total_investment / min(len(optimal_opportunities), 5)
        
        for opportunity in optimal_opportunities[:5]:
            try:
                # Provide liquidity
                result = await crypto.provide_liquidity(
                    protocol=opportunity.protocol,
                    pool=opportunity.pool,
                    amount=allocation_per_protocol,
                    token=opportunity.token
                )
                
                # Track position
                await track_defi_position(
                    wallet_id=wallet_id,
                    protocol=opportunity.protocol,
                    position_id=result.position_id,
                    amount=allocation_per_protocol
                )
                
            except DeFiError as e:
                logger.error(f"DeFi operation failed: {str(e)}")
    
    elif strategy == "lending":
        # Automated lending across protocols
        stable_coins = wallet.get_stablecoin_balance()
        
        # Find best lending rates
        lending_rates = await crypto.get_lending_rates(
            protocols=["compound", "aave", "maker"],
            tokens=["USDC", "DAI", "USDT"]
        )
        
        # Lend to highest-yield protocols
        for token, balance in stable_coins.items():
            if balance > 100:  # Minimum lending amount
                best_protocol = max(
                    lending_rates[token],
                    key=lambda x: x.apy
                )
                
                await crypto.lend_tokens(
                    protocol=best_protocol.protocol,
                    token=token,
                    amount=balance * 0.8  # Keep 20% liquid
                )

# Cross-chain bridge automation
async def optimize_cross_chain_transfers(transfers: list):
    """Optimize transfers across different blockchains"""
    
    for transfer in transfers:
        # Find optimal bridge
        bridge_options = await crypto.get_bridge_options(
            from_chain=transfer["from_chain"],
            to_chain=transfer["to_chain"],
            token=transfer["token"],
            amount=transfer["amount"]
        )
        
        # Select best bridge based on cost and speed
        optimal_bridge = min(
            bridge_options,
            key=lambda x: x.cost + (x.time_minutes * transfer.get("urgency_factor", 0.1))
        )
        
        # Execute transfer
        bridge_tx = await crypto.execute_bridge_transfer(
            bridge=optimal_bridge.bridge,
            from_chain=transfer["from_chain"],
            to_chain=transfer["to_chain"],
            token=transfer["token"],
            amount=transfer["amount"],
            recipient=transfer["recipient"]
        )
        
        # Monitor transfer
        await monitor_bridge_transfer(bridge_tx.tx_id)
        
        # Notify completion
        await send_notification(
            user_id=transfer["user_id"],
            type="transfer_completed",
            data={
                "amount": transfer["amount"],
                "token": transfer["token"],
                "from_chain": transfer["from_chain"],
                "to_chain": transfer["to_chain"],
                "tx_hash": bridge_tx.tx_hash
            }
        )

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from aimatrix.tools.financial import NFTTool

# Initialize NFT tools
nft = NFTTool(
    marketplaces=["opensea", "rarible", "foundation", "superrare"],
    blockchains=["ethereum", "polygon", "arbitrum"],
    ai_valuation=True
)

# Automated NFT portfolio management
async def manage_nft_portfolio(owner_address: str, strategy: dict):
    """Manage NFT portfolio with AI-driven strategies"""
    
    # Get current NFT holdings
    portfolio = await nft.get_portfolio(owner_address)
    
    # AI valuation and market analysis
    for nft_item in portfolio.nfts:
        # Get AI-powered valuation
        valuation = await nft.get_ai_valuation(
            contract_address=nft_item.contract_address,
            token_id=nft_item.token_id,
            factors=["rarity", "historical_sales", "collection_performance", "market_trends"]
        )
        
        # Update portfolio with valuations
        nft_item.estimated_value = valuation.estimated_value
        nft_item.confidence_score = valuation.confidence_score
        nft_item.recommendation = valuation.recommendation  # "hold", "sell", "buy_more"
    
    # Execute portfolio rebalancing based on strategy
    if strategy["type"] == "profit_taking":
        # Sell overvalued NFTs
        for nft_item in portfolio.nfts:
            if (nft_item.recommendation == "sell" and 
                nft_item.estimated_value > nft_item.purchase_price * strategy["profit_threshold"]):
                
                # List NFT for sale
                listing = await nft.create_listing(
                    marketplace="opensea",
                    contract_address=nft_item.contract_address,
                    token_id=nft_item.token_id,
                    price=nft_item.estimated_value * 0.95,  # 5% below estimated value
                    duration_days=strategy["listing_duration_days"]
                )
                
                # Track listing
                await track_nft_listing(listing.id)
    
    elif strategy["type"] == "collection_building":
        # Identify undervalued NFTs in target collections
        target_collections = strategy["target_collections"]
        budget = strategy["budget"]
        
        for collection in target_collections:
            # Find undervalued NFTs
            opportunities = await nft.find_undervalued_nfts(
                collection=collection,
                max_price=budget / len(target_collections),
                min_rarity_rank=strategy.get("min_rarity_rank", 1000)
            )
            
            # Auto-bid on opportunities
            for opportunity in opportunities[:strategy.get("max_bids_per_collection", 5)]:
                bid_amount = opportunity.estimated_value * 0.8  # Bid 20% below estimated value
                
                bid = await nft.place_bid(
                    marketplace="opensea",
                    contract_address=opportunity.contract_address,
                    token_id=opportunity.token_id,
                    bid_amount=bid_amount,
                    expiration_hours=24
                )
                
                await track_nft_bid(bid.id)

# NFT minting and launch automation
async def automate_nft_launch(collection_data: dict):
    """Automate NFT collection creation and launch"""
    
    # Generate NFT metadata and assets
    nfts = []
    
    for i in range(collection_data["collection_size"]):
        # AI-generated NFT attributes
        attributes = await ai_agent.generate_nft_attributes(
            collection_theme=collection_data["theme"],
            rarity_distribution=collection_data["rarity_distribution"],
            existing_attributes=[nft["attributes"] for nft in nfts]
        )
        
        # Generate artwork
        artwork = await ai_agent.generate_nft_artwork(
            attributes=attributes,
            style=collection_data["art_style"],
            resolution=collection_data["resolution"]
        )
        
        # Upload to IPFS
        ipfs_hash = await nft.upload_to_ipfs(artwork)
        
        # Create metadata
        metadata = {
            "name": f"{collection_data['name']} #{i+1}",
            "description": collection_data["description"],
            "image": f"ipfs://{ipfs_hash}",
            "attributes": attributes
        }
        
        # Upload metadata to IPFS
        metadata_hash = await nft.upload_metadata_to_ipfs(metadata)
        
        nfts.append({
            "token_id": i + 1,
            "metadata_uri": f"ipfs://{metadata_hash}",
            "attributes": attributes,
            "rarity_score": calculate_rarity_score(attributes, collection_data["rarity_weights"])
        })
    
    # Deploy smart contract
    contract = await nft.deploy_contract(
        name=collection_data["name"],
        symbol=collection_data["symbol"],
        max_supply=collection_data["collection_size"],
        mint_price=collection_data["mint_price"],
        royalty_percentage=collection_data["royalty_percentage"],
        reveal_strategy=collection_data["reveal_strategy"]
    )
    
    # Set up minting website
    minting_site = await nft.create_minting_website(
        contract_address=contract.address,
        collection_data=collection_data,
        nfts=nfts
    )
    
    # Launch marketing campaign
    await launch_nft_marketing_campaign(
        collection_data=collection_data,
        contract_address=contract.address,
        minting_site_url=minting_site.url
    )
    
    return {
        "contract_address": contract.address,
        "minting_site": minting_site.url,
        "total_nfts": len(nfts),
        "launch_status": "success"
    }

# NFT analytics and insights
async def generate_nft_market_insights():
    """Generate comprehensive NFT market analysis"""
    
    # Collect market data
    market_data = await nft.get_market_data(
        timeframe="30d",
        include_collections=["top_100", "trending", "new_launches"]
    )
    
    # AI-powered analysis
    insights = await ai_agent.analyze_nft_market(market_data)
    
    report = {
        "market_overview": {
            "total_volume": market_data.total_volume,
            "volume_change": market_data.volume_change_percentage,
            "average_price": market_data.average_price,
            "price_change": market_data.price_change_percentage,
            "active_traders": market_data.active_traders
        },
        "trending_collections": insights.trending_collections,
        "emerging_artists": insights.emerging_artists,
        "investment_opportunities": insights.investment_opportunities,
        "risk_alerts": insights.risk_alerts,
        "predictions": {
            "next_30_days": insights.short_term_predictions,
            "next_quarter": insights.medium_term_predictions
        }
    }
    
    return report

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from aimatrix.tools.financial import TaxCalculationTool

# Initialize tax calculation engine
tax_calculator = TaxCalculationTool(
    jurisdictions=["US", "EU", "UK", "CA", "AU", "SG", "JP"],
    real_time_rates=True,
    compliance_monitoring=True
)

# Automated tax calculation for transactions
async def calculate_transaction_taxes(transaction_data: dict):
    """Calculate applicable taxes for financial transactions"""
    
    # Determine tax jurisdictions
    jurisdictions = await tax_calculator.determine_jurisdictions(
        customer_location=transaction_data["customer_location"],
        business_location=transaction_data["business_location"],
        transaction_type=transaction_data["transaction_type"],
        product_category=transaction_data.get("product_category")
    )
    
    tax_breakdown = {}
    
    for jurisdiction in jurisdictions:
        # Get applicable tax rates
        tax_rates = await tax_calculator.get_tax_rates(
            jurisdiction=jurisdiction,
            transaction_date=transaction_data["date"],
            transaction_type=transaction_data["transaction_type"],
            amount=transaction_data["amount"]
        )
        
        # Calculate taxes
        taxes = {}
        
        if "VAT" in tax_rates:
            vat_amount = transaction_data["amount"] * tax_rates["VAT"] / 100
            taxes["VAT"] = {
                "rate": tax_rates["VAT"],
                "amount": vat_amount,
                "description": f"Value Added Tax ({jurisdiction})"
            }
        
        if "sales_tax" in tax_rates:
            sales_tax_amount = transaction_data["amount"] * tax_rates["sales_tax"] / 100
            taxes["sales_tax"] = {
                "rate": tax_rates["sales_tax"],
                "amount": sales_tax_amount,
                "description": f"Sales Tax ({jurisdiction})"
            }
        
        if "withholding_tax" in tax_rates:
            withholding_amount = transaction_data["amount"] * tax_rates["withholding_tax"] / 100
            taxes["withholding_tax"] = {
                "rate": tax_rates["withholding_tax"],
                "amount": withholding_amount,
                "description": f"Withholding Tax ({jurisdiction})"
            }
        
        tax_breakdown[jurisdiction] = {
            "taxes": taxes,
            "total_tax": sum(tax["amount"] for tax in taxes.values()),
            "net_amount": transaction_data["amount"] - sum(tax["amount"] for tax in taxes.values())
        }
    
    return tax_breakdown

# Cryptocurrency tax reporting
async def generate_crypto_tax_report(customer_id: str, tax_year: int):
    """Generate comprehensive cryptocurrency tax report"""
    
    # Get all crypto transactions for the year
    crypto_transactions = await crypto.get_transactions(
        customer_id=customer_id,
        start_date=datetime(tax_year, 1, 1),
        end_date=datetime(tax_year, 12, 31)
    )
    
    # Calculate gains/losses for each transaction
    tax_events = []
    
    for transaction in crypto_transactions:
        if transaction.type in ["sell", "trade", "spend"]:
            # Calculate cost basis
            cost_basis = await crypto.calculate_cost_basis(
                customer_id=customer_id,
                asset=transaction.asset,
                quantity=transaction.quantity,
                date=transaction.date,
                method="FIFO"  # or "LIFO", "specific_identification"
            )
            
            # Get fair market value at transaction date
            fmv = await crypto.get_historical_price(
                asset=transaction.asset,
                date=transaction.date
            )
            
            # Calculate gain/loss
            proceeds = transaction.quantity * fmv
            gain_loss = proceeds - cost_basis.total_cost
            
            tax_event = {
                "date": transaction.date,
                "type": transaction.type,
                "asset": transaction.asset,
                "quantity": transaction.quantity,
                "cost_basis": cost_basis.total_cost,
                "fair_market_value": fmv,
                "proceeds": proceeds,
                "gain_loss": gain_loss,
                "holding_period": (transaction.date - cost_basis.acquisition_date).days,
                "term": "long" if (transaction.date - cost_basis.acquisition_date).days > 365 else "short"
            }
            
            tax_events.append(tax_event)
    
    # Summarize tax implications
    summary = {
        "short_term_gains": sum(e["gain_loss"] for e in tax_events if e["term"] == "short" and e["gain_loss"] > 0),
        "short_term_losses": sum(abs(e["gain_loss"]) for e in tax_events if e["term"] == "short" and e["gain_loss"] < 0),
        "long_term_gains": sum(e["gain_loss"] for e in tax_events if e["term"] == "long" and e["gain_loss"] > 0),
        "long_term_losses": sum(abs(e["gain_loss"]) for e in tax_events if e["term"] == "long" and e["gain_loss"] < 0),
        "total_transactions": len(tax_events)
    }
    
    # Calculate net gains/losses
    summary["net_short_term"] = summary["short_term_gains"] - summary["short_term_losses"]
    summary["net_long_term"] = summary["long_term_gains"] - summary["long_term_losses"]
    summary["net_capital_gains"] = summary["net_short_term"] + summary["net_long_term"]
    
    # Generate tax forms
    tax_forms = await tax_calculator.generate_crypto_tax_forms(
        customer_id=customer_id,
        tax_year=tax_year,
        tax_events=tax_events,
        jurisdiction="US"  # or customer's jurisdiction
    )
    
    return {
        "tax_events": tax_events,
        "summary": summary,
        "tax_forms": tax_forms,
        "recommendations": await generate_tax_optimization_recommendations(summary)
    }

# Automated compliance monitoring
async def monitor_compliance_requirements(customer_id: str):
    """Monitor and ensure ongoing tax compliance"""
    
    customer = await get_customer(customer_id)
    
    # Check various compliance requirements
    compliance_status = {
        "tax_reporting": {},
        "regulatory_filing": {},
        "documentation": {},
        "alerts": []
    }
    
    # Tax reporting requirements
    if customer.business_type == "corporation":
        # Check quarterly tax filings
        current_quarter = get_current_quarter()
        
        quarterly_filing = await tax_calculator.check_quarterly_filing_status(
            customer_id=customer_id,
            quarter=current_quarter
        )
        
        if not quarterly_filing.filed and quarterly_filing.due_date < datetime.now():
            compliance_status["alerts"].append({
                "type": "overdue_filing",
                "message": f"Quarterly tax filing for Q{current_quarter} is overdue",
                "severity": "high",
                "action_required": "File quarterly return immediately"
            })
    
    # Transaction reporting thresholds
    large_transactions = await get_large_transactions(
        customer_id=customer_id,
        threshold=10000,  # $10,000 threshold for reporting
        timeframe="last_30_days"
    )
    
    for transaction in large_transactions:
        reporting_requirements = await tax_calculator.check_reporting_requirements(
            transaction=transaction,
            customer_jurisdiction=customer.jurisdiction
        )
        
        if reporting_requirements.ctrs_required and not transaction.ctrs_filed:
            compliance_status["alerts"].append({
                "type": "ctrs_filing_required",
                "message": f"Currency Transaction Report required for transaction {transaction.id}",
                "severity": "medium",
                "deadline": reporting_requirements.filing_deadline
            })
    
    # International compliance (FATCA, CRS)
    if customer.has_international_exposure:
        international_compliance = await tax_calculator.check_international_compliance(
            customer_id=customer_id,
            year=datetime.now().year
        )
        
        if international_compliance.fatca_reporting_required:
            compliance_status["regulatory_filing"]["fatca"] = {
                "required": True,
                "deadline": international_compliance.fatca_deadline,
                "status": international_compliance.fatca_status
            }
        
        if international_compliance.crs_reporting_required:
            compliance_status["regulatory_filing"]["crs"] = {
                "required": True,
                "deadline": international_compliance.crs_deadline,
                "status": international_compliance.crs_status
            }
    
    return compliance_status

# Automated tax optimization
async def optimize_tax_strategy(customer_id: str, strategy_type: str):
    """AI-powered tax optimization strategies"""
    
    customer_profile = await get_customer_tax_profile(customer_id)
    
    if strategy_type == "loss_harvesting":
        # Cryptocurrency tax loss harvesting
        crypto_positions = await crypto.get_positions(customer_id)
        
        harvesting_opportunities = []
        
        for position in crypto_positions:
            current_value = await crypto.get_current_value(
                asset=position.asset,
                quantity=position.quantity
            )
            
            if current_value < position.cost_basis:
                # Potential loss harvesting opportunity
                unrealized_loss = position.cost_basis - current_value
                
                # Check wash sale rules
                wash_sale_risk = await tax_calculator.check_wash_sale_risk(
                    customer_id=customer_id,
                    asset=position.asset,
                    sale_date=datetime.now()
                )
                
                if not wash_sale_risk.has_risk:
                    harvesting_opportunities.append({
                        "asset": position.asset,
                        "quantity": position.quantity,
                        "cost_basis": position.cost_basis,
                        "current_value": current_value,
                        "unrealized_loss": unrealized_loss,
                        "tax_savings": unrealized_loss * customer_profile.marginal_tax_rate
                    })
        
        # Execute loss harvesting
        for opportunity in harvesting_opportunities:
            if opportunity["tax_savings"] > 100:  # Minimum savings threshold
                # Sell position to realize loss
                await crypto.sell_position(
                    customer_id=customer_id,
                    asset=opportunity["asset"],
                    quantity=opportunity["quantity"]
                )
                
                # Schedule repurchase after wash sale period (31 days)
                await schedule_repurchase(
                    customer_id=customer_id,
                    asset=opportunity["asset"],
                    quantity=opportunity["quantity"],
                    delay_days=31
                )
        
        return harvesting_opportunities
    
    elif strategy_type == "income_timing":
        # Optimize timing of income recognition
        pending_income = await get_pending_income_events(customer_id)
        
        recommendations = []
        
        for income_event in pending_income:
            # Analyze optimal timing
            current_year_impact = await tax_calculator.calculate_tax_impact(
                income=income_event.amount,
                year=datetime.now().year,
                customer_profile=customer_profile
            )
            
            next_year_impact = await tax_calculator.calculate_tax_impact(
                income=income_event.amount,
                year=datetime.now().year + 1,
                customer_profile=customer_profile
            )
            
            if next_year_impact.effective_rate < current_year_impact.effective_rate:
                recommendations.append({
                    "income_event": income_event,
                    "recommendation": "defer_to_next_year",
                    "tax_savings": current_year_impact.total_tax - next_year_impact.total_tax,
                    "rationale": "Lower expected tax rate next year"
                })
        
        return recommendations