Trading Behavior Audit System¶

37.1 System Overview¶

The Trading Behavior Audit System provides comprehensive end-to-end audit trails for all trading activities, ensuring full compliance with regulatory requirements and internal controls. It records every trading event with timestamps, source tracking, and processing paths, enabling complete traceability and automated audit reporting.

37.1.1 Core Objectives¶

• Full Event Recording: Complete audit trail for orders, cancellations, executions, strategy signals, risk controls, and anomalies
• Traceability: Support retrospective analysis of trading chains and system actions
• Audit Queries: Search and retrieve complete audit chains by account, strategy, or order ID
• Automated Reporting: Generate comprehensive audit reports with anomaly highlighting
• Regulatory Compliance: Meet requirements of major brokers, asset managers, and regulatory bodies

37.2 Architecture Design¶

37.2.1 Microservice Architecture¶

Trade Audit Center Service:

services/trade-audit-center/
├── src/
│   ├── main.py
│   ├── recorder/
│   │   ├── audit_recorder.py
│   ├── database/
│   │   ├── audit_db.py
│   ├── query/
│   │   ├── audit_query_engine.py
│   ├── analyzer/
│   │   ├── audit_analyzer.py
│   ├── reporter/
│   │   ├── audit_reporter.py
│   ├── api/
│   │   ├── audit_api.py
│   ├── config.py
│   ├── requirements.txt
├── Dockerfile

37.2.2 Core Components¶

• Audit Event Recorder: Real-time recording of all trading-related actions
• Audit Database: High-performance storage and indexing of audit records
• Audit Query Engine: Fast search and retrieval of specified trading chains
• Audit Analyzer: Automatic detection of anomalies and high-latency chains
• Report Generator: Automated PDF audit report generation
• API Interface: Query audit chains and generate reports
• Frontend Dashboard: Audit chain browser and anomaly heatmap visualization

37.3 Module Design¶

37.3.1 Audit Event Recorder (audit_recorder.py)¶

• Records every trading event with complete details

import time

class AuditRecorder:
    def record_event(self, event_type, entity_id, details):
        event = {
            "timestamp": time.time(),
            "event_type": event_type,
            "entity_id": entity_id,
            "details": details
        }
        audit_db.save_event(event)

37.3.2 Audit Database (audit_db.py)¶

• High-performance NoSQL storage for audit events

from pymongo import MongoClient

class AuditDB:
    def __init__(self):
        self.client = MongoClient("mongodb://localhost:27017")
        self.db = self.client["audit_logs"]

    def save_event(self, event):
        self.db.events.insert_one(event)

    def search_events(self, filters):
        return list(self.db.events.find(filters))

37.3.3 Audit Query Engine (audit_query_engine.py)¶

• Retrieves complete audit chains by various criteria

class AuditQueryEngine:
    def get_chain_by_order(self, order_id):
        return audit_db.search_events({"entity_id": order_id})

37.3.4 Audit Analyzer (audit_analyzer.py)¶

• Analyzes audit chains for anomalies and quality issues

class AuditAnalyzer:
    def detect_anomalies(self, event_chain):
        anomalies = []
        for event in event_chain:
            if event["event_type"] == "execution" and event["details"]["slippage"] > 0.005:
                anomalies.append(event)
        return anomalies

37.3.5 Report Generator (audit_reporter.py)¶

• Generates comprehensive PDF audit reports

from fpdf import FPDF

class AuditReporter:
    def generate_report(self, event_chain):
        pdf = FPDF()
        pdf.add_page()
        pdf.set_font("Arial", size=12)
        for event in event_chain:
            pdf.cell(0, 10, f"{event['timestamp']} {event['event_type']} {event['details']}", ln=True)
        pdf.output("audit_report.pdf")

37.3.6 API Interface (audit_api.py)¶

• FastAPI endpoints for audit queries and report generation

from fastapi import APIRouter
router = APIRouter()

@router.get("/audit/chain/{order_id}")
async def get_audit_chain(order_id: str):
    return audit_query_engine.get_chain_by_order(order_id)

@router.post("/audit/report/{order_id}")
async def generate_audit_report(order_id: str):
    chain = audit_query_engine.get_chain_by_order(order_id)
    audit_reporter.generate_report(chain)
    return {"message": "Report generated"}

37.3.7 Frontend Dashboard¶

• Single order audit chain visualization (signal to risk control to order to execution)
• Anomaly event heatmap (latency, slippage, risk control blocks)
• One-click audit report export

37.4 Audit Flow Example¶

1. Trading system generates events → trade-audit-center records
2. Audit DB stores events → query engine supports traceability
3. Analyzer processes audit chains → detects anomalies
4. Reporter generates comprehensive audit reports

37.5 Technology Stack¶

• Python (FastAPI, pymongo): Service implementation
• MongoDB: High-performance audit data storage
• Docker: Containerization
• React/TypeScript: Frontend dashboard
• FPDF: PDF report generation

37.6 API Design¶

• GET /audit/chain/{order_id}: Get complete audit chain for order
• GET /audit/account/{account_id}: Get audit chains for account
• GET /audit/strategy/{strategy_id}: Get audit chains for strategy
• POST /audit/report/{order_id}: Generate audit report for order
• GET /audit/anomalies: Get detected anomalies

37.7 Frontend Integration¶

• Real-time audit chain visualization
• Anomaly detection and reporting
• Audit report generation and export

37.8 Implementation Roadmap¶

• Phase 1: Basic event recording and database storage
• Phase 2: Query engine and audit chain retrieval
• Phase 3: Anomaly detection and automated reporting

37.9 Integration with Existing System¶

• Integrates with all trading services for comprehensive event capture
• Provides audit capabilities for compliance and regulatory requirements

37.10 Business Value¶