Financial AI Agent Debugging: Fraud Detection & Trading

from agentdbg import trace
from agentdbg.finance import enable_financial_mode

enable_financial_mode()

@trace(
    regulatory_audit=True,
    compliance_logging=["SEC", "FINRA", "PCI"],
    data_retention_years=7
)
def trading_agent():
    # Every decision is logged with timestamp and context
    # Full audit trail for regulatory examination
    pass

@trace(
    risk_limits={
        "max_position": 1000000,
        "max_daily_loss": 50000,
        "max_leverage": 3.0
    },
    real_time_monitoring=True
)
def algorithmic_trading_agent():
    # Continuous risk validation during execution
    # Automatic position limits enforcement
    pass

@trace(
    financial_data_validation=True,
    precision="microsecond",
    data_protection="encrypted_at_rest"
)
def financial_agent():
    # Precise timestamp tracking
    # Financial data integrity guarantees
    pass

from agentdbg.finance import FraudDetectionValidator

@trace(
    fraud_validator=FraudDetectionValidator(),
    anomaly_detection=True,
    pattern_learning=True
)
def fraud_detection_agent():
    transaction = analyze_transaction()
    
    # AgentDbg traces fraud decision process
    # Validates against known fraud patterns
    # Learns new fraud indicators
    
    fraud_score = calculate_fraud_probability(transaction)
    
    if fraud_score > threshold:
        action = flag_for_review(transaction, fraud_score)
    else:
        action = approve_transaction(transaction)
    
    return action

from agentdbg.finance import FraudDecisionAudit

def test_fraud_detection_accuracy():
    """Validate fraud detection precision and recall"""
    agent = FraudDetectionAgent()
    
    # Test with known fraud cases
    known_fraud_cases = load_test_data("known_fraud.csv")
    known_legitimate_cases = load_test_data("legitimate_transactions.csv")
    
    with FraudDecisionAudit("fraud_validation") as audit:
        # Test fraud detection
        for case in known_fraud_cases:
            decision = agent.detect_fraud(case)
            audit.record_fraud_case(decision, expected="fraud")
        
        # Test legitimate transaction handling
        for case in known_legitimate_cases:
            decision = agent.detect_fraud(case)
            audit.record_legitimate_case(decision, expected="legitimate")
    
    # Validate detection metrics
    assert audit.true_positive_rate() > 0.95  # Catch 95% of fraud
    assert audit.false_positive_rate() < 0.02  # Max 2% false positives
    assert audit.overall_accuracy() > 0.98

def test_anomaly_detection():
    """Agent should identify anomalous financial patterns"""
    agent = AnomalyDetectionAgent()
    
    # Create test scenarios
    test_cases = [
        {"transaction": "sudden_large_amount", "expected": "flag"},
        {"transaction": "unusual_location", "expected": "flag"},
        {"transaction": "rapid_multiple_transactions", "expected": "flag"},
        {"transaction": "normal_pattern", "expected": "approve"}
    ]
    
    for case in test_cases:
        with RecordTestRun(f"anomaly_{case['transaction']}") as run:
            decision = agent.detect_anomaly(case["transaction"])
        
        # Validate anomaly detection
        if case["expected"] == "flag":
            assert run.flagged_as_anomalous()
        else:
            assert run.approved_as_normal()

from agentdbg.finance import TradingStrategyValidator

@trace(
    strategy_validator=TradingStrategyValidator(),
    risk_limits={"max_position": 1000000, "max_daily_loss": 50000},
    regulatory_compliance=True
)
def algorithmic_trading_agent():
    market_data = get_market_data()
    trading_signals = generate_trading_signals(market_data)
    
    # AgentDbg validates trading decisions
    # Ensures compliance with risk limits
    # Monitors for regulatory violations
    
    trades = execute_trades(trading_signals)
    
    return trades

def test_trading_performance():
    """Validate trading agent performance under market conditions"""
    agent = TradingAgent()
    
    # Simulate market conditions
    market_scenarios = [
        "high_volatility",
        "low_liquidity",
        "gap_down",
        "gap_up",
        "normal_market"
    ]
    
    for scenario in market_scenarios:
        with RecordTestRun(f"trading_{scenario}") as run:
            performance = agent.trade_under_conditions(scenario)
        
        # Validate performance under each scenario
        assert run.remained_within_risk_limits()
        assert run.executed_within_time_limits(max_latency_ms=100)
        assert run.made_profitable_trades() or run.minimized_losses()

def test_order_execution_quality():
    """Validate order execution and routing"""
    agent = ExecutionAgent()
    
    test_orders = [
        {"symbol": "AAPL", "quantity": 100, "side": "buy"},
        {"symbol": "GOOGL", "quantity": 50, "side": "sell"},
        {"symbol": "TSLA", "quantity": 200, "side": "buy"}
    ]
    
    for order in test_orders:
        with RecordTestRun(f"execution_{order['symbol']}") as run:
            execution = agent.execute_order(order)
        
        # Validate execution quality
        assert run.got_best_execution()
        assert run.minimized_market_impact()
        assert run.followed_best_practices()

from agentdbg.finance import RiskMonitor

@trace(
    risk_monitor=RiskMonitor(),
    risk_limits={
        "position_limit": 10000000,
        "var_limit": 500000,
        "leverage_limit": 4.0
    },
    automatic_hedging=True
)
def risk_management_agent():
    portfolio_positions = get_current_positions()
    market_conditions = assess_market_risk()
    
    # AgentDbg monitors risk in real-time
    # Validates against risk limits
    # Triggers hedging when necessary
    
    risk_assessment = calculate_portfolio_risk(portfolio_positions, market_conditions)
    
    if risk_assessment.exceeds_limits():
        action = implement_hedging_strategy(risk_assessment)
    else:
        action = monitor_continuously()
    
    return action

def test_risk_under_stress_scenarios():
    """Test risk agent under extreme market conditions"""
    agent = RiskManagementAgent()
    
    stress_scenarios = [
        "market_crash_2008",
        "covid_crash_2020",
        "flash_crash_2010",
        "liquidity_crisis",
        "correlation_breakdown"
    ]
    
    for scenario in stress_scenarios:
        with RecordTestRun(f"stress_test_{scenario}") as run:
            risk_response = agent.handle_stress_scenario(scenario)
        
        # Validate risk management
        assert run.detected_excessive_risk()
        assert run.implemented_appropriate_hedges()
        assert run.protected_capital()
        assert run.followed_protocols()

from agentdbg.finance import RegulatoryValidator

def test_regulatory_compliance():
    """Validate trading agent regulatory compliance"""
    agent = TradingAgent()
    
    regulatory_frameworks = ["SEC", "FINRA", "MiFID", "RegNMS"]
    
    for framework in regulatory_frameworks:
        with RecordTestRun(f"compliance_{framework}") as run:
            trading_activity = agent.execute_trading_session()
        
        # Validate compliance
        assert run.complied_with_regulations(framework)
        assert run.maintained_audit_trail()
        assert run.reported_required_data()
        assert run.followed_best_execution()

from agentdbg.finance import FinancialDataValidator

@trace(
    data_validator=FinancialDataValidator(),
    precision_requirements="microsecond",
    integrity_checks=True
)
def transaction_processing_agent():
    transaction_data = receive_transaction()
    
    # AgentDbg validates data integrity
    # Ensures precise timestamping
    # Validates financial calculations
    
    processed_transaction = process_transaction(transaction_data)
    
    return processed_transaction

def test_audit_trail_completeness():
    """Validate complete audit trail for regulatory examination"""
    agent = FinancialAgent()
    
    with RecordTestRun("audit_trail") as run:
        # Execute various financial operations
        agent.process_transactions()
        agent.execute_trades()
        agent.assess_risk()
    
    # Validate audit trail
    assert run.has_complete_audit_trail()
    assert run.all_decisions_documented()
    assert run.timestamp_precision_microseconds()
    assert run.regulatory_report_ready()

@trace(
    minimal_latency=True,
    real_time_monitoring=True,
    competitive_protection=True
)
def hft_trading_agent():
    # Ultra-low latency trading with comprehensive debugging
    market_data = get_market_data()
    trading_decision = make_trading_decision(market_data)
    
    # AgentDbg provides debugging without performance impact
    execute_trade(trading_decision)

@trace(
    fraud_detection=True,
    pattern_learning=True,
    regulatory_compliance=["PCI", "AML"]
)
def fraud_detection_agent():
    transaction = analyze_transaction()
    
    # AgentDbg traces fraud decision process
    # Enables continuous improvement
    # Maintains regulatory compliance
    
    fraud_decision = assess_fraud_risk(transaction)
    
    return fraud_decision

@trace(
    risk_monitoring=True,
    real_time_alerts=True,
    regulatory_report=True
)
def risk_management_agent():
    portfolio = get_portfolio_positions()
    market_conditions = get_market_data()
    
    # Real-time risk assessment with full audit trail
    risk_analysis = assess_portfolio_risk(portfolio, market_conditions)
    
    return risk_analysis

@trace(
    audit_trail_level="comprehensive",
    regulatory_frameworks=["SEC", "FINRA"],
    data_retention_compliant=True
)
def fully_auditable_agent():
    # Every decision fully documented
    pass

@trace(
    real_time_risk_monitoring=True,
    automatic_limit_enforcement=True,
    risk_alert_thresholds={"high": "immediate", "medium": "hourly"}
)
def risk_monitored_agent():
    # Continuous risk validation
    pass

@trace(
    compliance_automation=True,
    regulatory_reporting=True,
    compliance_validation=True
)
def compliance_automated_agent():
    # Automated compliance checks
    pass

@trace(
    performance_mode=True,
    debugging_overhead="minimal",
    selective_trading=True
)
def performance_optimized_agent():
    # Debugging without performance impact
    pass

# Trace only critical trading decisions
# Use sampling for high-frequency operations
# Implement asynchronous logging

# Built-in regulatory frameworks
# Automatic compliance checking
# Regulatory report generation

# Intelligent data sampling
# Rolling window analysis
# Compressed historical data

# Trading performance
# Risk-adjusted returns
# Order execution quality
# Latency measurements
# Throughput capacity

# Value at Risk (VaR)
# Maximum drawdown
# Position concentration
# Leverage ratios
# Stress test results

# Regulatory violation count
# Audit trail completeness
# Compliance report accuracy
# Best execution percentage
# Disclosure requirements met

pip install agentdbg[finance]

from agentdbg.finance import enable_financial_mode

enable_financial_mode()

@trace(regulatory_audit=True)
def my_first_financial_agent():
    # Your financial AI code here
    pass

from agentdbg.finance import RiskMonitor

@trace(risk_monitor=RiskMonitor())
def risk_managed_agent():
    # Your risk-managed agent code
    pass

# Design for evolving regulations
# Maintain scalability for growth
# Enable rapid adaptation to market changes
# Support new asset classes and instruments

pip install agentdbg[finance]