시스템 아키텍처

분산 처리와 마이크로서비스 기반의 고가용성 시스템

시스템 전체 구조

┌─────────────────────────────────────────────────────────────────────┐
│                         Load Balancer (Nginx)                       │
│                     SSL Termination | Rate Limiting                 │
└────────────────────────────┬────────────────────────────────────────┘
                             │
          ┌──────────────────┼──────────────────┐
          │                  │                  │
┌─────────▼────────┐ ┌──────▼───────┐ ┌───────▼────────┐
│  API Gateway 1   │ │ API Gateway 2│ │ API Gateway 3  │
│ (Node.js/Express)│ │ (Hot Standby)│ │ (Failover)     │
└─────────┬────────┘ └──────┬───────┘ └───────┬────────┘
          │                  │                  │
          └──────────────────┼──────────────────┘
                             │
          ┌──────────────────┴──────────────────┐
          │        Message Queue (Kafka)        │
          │    Topic: trades, analysis, logs    │
          └──────────────────┬──────────────────┘
                             │
    ┌────────────────────────┼────────────────────────┐
    │                        │                        │
┌───▼──────────────┐  ┌─────▼────────────┐  ┌───────▼──────────┐
│ Trading Engine   │  │ AI Engine        │  │ Data Collector   │
│ (Python/FastAPI) │  │ (PyTorch/TF)     │  │ (Worker Cluster) │
│                  │  │                  │  │                  │
│ - Order Exec     │  │ - 54 AI Models   │  │ - 20+ Workers    │
│ - Risk Mgmt      │  │ - Ensemble Vote  │  │ - WebSocket      │
│ - Position Mgmt  │  │ - Backtesting    │  │ - REST API       │
└───┬──────────────┘  └─────┬────────────┘  └───────┬──────────┘
    │                        │                        │
    │                        │                        │
┌───▼────────────────────────▼────────────────────────▼──────────┐
│                     Redis Cluster (Cache)                       │
│         Hot Data | Session | Real-time Market Data             │
└────────────────────────────┬────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────┐
│              PostgreSQL Cluster (Primary Data)                  │
│  Master(Write) | Replica1(Read) | Replica2(Read)                │
│  - Trades | Users | Positions | Analysis Results                │
└────────────────────────────┬────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────┐
│           TimescaleDB (Time-series Data)                        │
│  - Market Ticks | OHLCV | Technical Indicators                  │
│  - Retention: 2 years | Compression: 90%                        │
└─────────────────────────────────────────────────────────────────┘

AI 엔진 아키텍처

54개 독립 AI 모델의 앙상블 의사결정 시스템

┌───────────────── AI Model Ensemble (54 Models) ─────────────────┐
│                                                                   │
│  ┌────────────┐  ┌────────────┐  ┌────────────┐  ┌──────────┐  │
│  │ LSTM (×12) │  │ GRU (×12)  │  │ Trans (×10)│  │ CNN (×8) │  │
│  │ Seq Length │  │ Hidden     │  │ Attention  │  │ Conv     │  │
│  │ 50-200     │  │ 128-512    │  │ 8-16 heads │  │ 3-7 kern │  │
│  └──────┬─────┘  └──────┬─────┘  └──────┬─────┘  └─────┬────┘  │
│         │                │                │              │        │
│         └────────────────┴────────────────┴──────────────┘        │
│                              │                                    │
│                     ┌────────▼─────────┐                          │
│                     │  Voting System   │                          │
│                     │  (Weighted Avg)  │                          │
│                     │                  │                          │
│                     │  Confidence >= 85%│                          │
│                     └────────┬─────────┘                          │
│                              │                                    │
│                     ┌────────▼─────────┐                          │
│                     │ Risk Assessment  │                          │
│                     │ - Kelly Criterion│                          │
│                     │ - Max Drawdown   │                          │
│                     │ - Sharpe Ratio   │                          │
│                     └────────┬─────────┘                          │
└──────────────────────────────┼──────────────────────────────────┘
                               │
                      ┌────────▼─────────┐
                      │  Trade Execution │
                      │  - Order Type    │
                      │  - Position Size │
                      │  - Stop Loss     │
                      └──────────────────┘
54
독립 AI 모델
85%+
의사결정 신뢰도
<50ms
추론 시간

데이터 처리 파이프라인

실시간 데이터 수집부터 분석까지의 전체 흐름

Data Sources (Multiple Exchanges)
    │
    │ ┌─────────────────────────────────────────┐
    └▶│  Worker Cluster (20+ Distributed PCs)  │
      │  - WebSocket Connections                 │
      │  - REST API Polling (1s interval)        │
      │  - Order Book Snapshots                  │
      └─────────────────┬───────────────────────┘
                        │
                ┌───────▼────────┐
                │  Kafka Ingestion│
                │  Partition: 12  │
                │  Replication: 3 │
                └───────┬────────┘
                        │
            ┌───────────┴───────────┐
            │                       │
    ┌───────▼───────┐      ┌───────▼────────┐
    │ Stream Proc   │      │  Batch Proc    │
    │ (Kafka Stream)│      │  (Apache Spark)│
    │                │      │                │
    │ - Filtering    │      │ - Aggregation  │
    │ - Enrichment   │      │ - Feature Eng  │
    │ - Validation   │      │ - ML Training  │
    └───────┬───────┘      └───────┬────────┘
            │                       │
            └───────────┬───────────┘
                        │
                ┌───────▼────────┐
                │  Feature Store │
                │  (Redis + S3)  │
                │                │
                │ - Raw: 7 days  │
                │ - Agg: 90 days │
                │ - Model: 2 yrs │
                └───────┬────────┘
                        │
                ┌───────▼────────┐
                │  AI Model API  │
                │  (Inference)   │
                └───────┬────────┘
                        │
                ┌───────▼────────┐
                │  Trade Signal  │
                └────────────────┘
20+
워커 노드
10K+
초당 이벤트
99.9%
데이터 정확도
<100ms
E2E 레이턴시

보안 아키텍처

다층 방어와 제로 트러스트 보안 모델

┌────────────────────── Security Layers ──────────────────────┐
│                                                               │
│  Layer 1: Network Security                                   │
│  ┌─────────────────────────────────────────────────────┐    │
│  │ WAF (CloudFlare) → DDoS Protection → Rate Limiting  │    │
│  │ Firewall Rules: Whitelist IP | GeoIP Blocking       │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                               │
│  Layer 2: Application Security                               │
│  ┌─────────────────────────────────────────────────────┐    │
│  │ JWT Authentication (RS256) | Session Management     │    │
│  │ RBAC (Role-Based Access) | API Key Rotation         │    │
│  │ SQL Injection Prevention | XSS Protection            │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                               │
│  Layer 3: Data Security                                      │
│  ┌─────────────────────────────────────────────────────┐    │
│  │ Encryption at Rest (AES-256) | In Transit (TLS 1.3) │    │
│  │ Key Management (AWS KMS) | Secret Rotation          │    │
│  │ Database Encryption | Backup Encryption              │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                               │
│  Layer 4: API Security                                       │
│  ┌─────────────────────────────────────────────────────┐    │
│  │ Read-Only API Keys | No Fund Withdrawal              │    │
│  │ IP Whitelist | Request Signing (HMAC-SHA256)        │    │
│  │ Audit Logging | Anomaly Detection                    │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                               │
│  Layer 5: Monitoring & Incident Response                     │
│  ┌─────────────────────────────────────────────────────┐    │
│  │ 24/7 Security Monitoring | SIEM Integration          │    │
│  │ Intrusion Detection | Automated Alerting             │    │
│  │ Incident Response Plan | Regular Security Audits    │    │
│  └─────────────────────────────────────────────────────┘    │
└───────────────────────────────────────────────────────────────┘

시스템 성능 지표

처리 성능

API Response Time (p95): 28ms API Response Time (p99): 45ms Trade Execution: <50ms Data Ingestion: 10,000 events/sec AI Inference: 35ms (avg) Database Query: <10ms (cached) WebSocket Latency: <20ms Throughput: 1,000+ trades/hour

안정성 지표

System Uptime: 99.95% MTBF: 2,800 hours MTTR: <15 minutes Error Rate: <0.01% Success Rate: 99.98% Data Accuracy: 99.99% Failover Time: <5 seconds Backup Frequency: Real-time

확장성

Horizontal Scaling: Auto (Kubernetes HPA) Max Worker Nodes: 100+ Load Balancing: Round Robin + Least Connection Database Sharding: Hash-based (User ID) Cache Hit Rate: 95%+ CDN Coverage: Global (20+ PoPs) Container Orchestration: Kubernetes 1.28 Service Mesh: Istio 1.20

핵심 기술 스택 상세

AI/ML Stack

// Deep Learning Frameworks PyTorch 2.1.0 (Primary) TensorFlow 2.14 (Secondary) ONNX Runtime (Inference) // ML Libraries scikit-learn 1.3.2 XGBoost 2.0.1 LightGBM 4.1.0 CatBoost 1.2.2 // Feature Engineering pandas 2.1.3 numpy 1.26.2 TA-Lib 0.4.28

Infrastructure Stack

// Container & Orchestration Kubernetes 1.28 Docker 24.0.7 Helm 3.13 // Service Mesh Istio 1.20 Envoy Proxy 1.28 // Monitoring Prometheus 2.48 Grafana 10.2 ELK Stack 8.11 Jaeger (Tracing)

Databases

// Primary Database PostgreSQL 16.1 - Replication: Streaming - HA: Patroni + etcd - Backup: pgBackRest // Time-series TimescaleDB 2.13 - Compression: 90% - Retention: 2 years // Cache Redis 7.2 Cluster - Nodes: 6 (3 master + 3 replica) - Eviction: LRU

Messaging

// Message Queue Apache Kafka 3.6 - Brokers: 3 - Partitions: 12 per topic - Replication Factor: 3 - Retention: 7 days // Stream Processing Kafka Streams 3.6 Apache Flink 1.18 // Real-time WebSocket (Socket.IO 4.7) Server-Sent Events (SSE)

DevOps & CI/CD 파이프라인

┌─── Developer Workflow ───┐
│  Git Push → GitHub        │
└─────────┬─────────────────┘
          │
┌─────────▼─────────────────────────────────────────────────┐
│ CI/CD Pipeline (GitHub Actions / GitLab CI)               │
│                                                             │
│  Stage 1: Build                                            │
│  ├─ Code Linting (pylint, eslint)                         │
│  ├─ Unit Tests (pytest, jest) → Coverage ≥ 80%            │
│  ├─ Security Scan (Snyk, Trivy)                           │
│  └─ Docker Image Build → Push to Registry                 │
│                                                             │
│  Stage 2: Test                                             │
│  ├─ Integration Tests                                      │
│  ├─ E2E Tests (Playwright)                                │
│  ├─ Performance Tests (k6)                                 │
│  └─ Security Tests (OWASP ZAP)                            │
│                                                             │
│  Stage 3: Deploy                                           │
│  ├─ Staging Environment Deploy                             │
│  ├─ Smoke Tests                                            │
│  ├─ Manual Approval (Production)                           │
│  ├─ Blue-Green Deployment                                  │
│  ├─ Canary Release (10% → 50% → 100%)                     │
│  └─ Health Check & Rollback if Failed                      │
│                                                             │
│  Stage 4: Monitor                                          │
│  ├─ Metrics Collection (Prometheus)                        │
│  ├─ Log Aggregation (ELK)                                 │
│  ├─ APM (Application Performance Monitoring)               │
│  └─ Alerting (PagerDuty, Slack)                           │
└─────────────────────────────────────────────────────────────┘
15min
평균 배포 시간
50+
주간 배포 횟수
0.1%
배포 실패율

재해 복구 계획 (DR)

RTO / RPO

Recovery Time Objective (RTO): <15 min Recovery Point Objective (RPO): <5 min Backup Strategy: ├─ Full Backup: Daily (00:00 UTC) ├─ Incremental: Every 6 hours ├─ Transaction Logs: Real-time └─ Cross-Region Replication: Yes DR Site: ├─ Location: Secondary Region ├─ Sync Method: Async Replication ├─ Failover: Automated └─ Testing: Monthly

고가용성 설계

Multi-AZ Deployment: ├─ Primary: ap-northeast-2a ├─ Secondary: ap-northeast-2b └─ Tertiary: ap-northeast-2c Redundancy: ├─ Load Balancers: 2+ (Active-Active) ├─ API Servers: 3+ (Multi-AZ) ├─ Databases: 1 Primary + 2 Replicas ├─ Cache: 6 Nodes (Cluster) └─ Message Queue: 3 Brokers Health Checks: ├─ Interval: 10 seconds ├─ Timeout: 5 seconds └─ Threshold: 3 failures

기술 상담 문의

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