DSTA Microservices Architecture Design¶
Version: 2.0
Last Updated: 2026-01-16
Status: Architecture Proposal
Author: DSTA Team
Table of Contents¶
- Executive Summary
- Current State Analysis
- Microservices Architecture Overview
- Service Catalog
- Data Architecture
- Communication Patterns
- Deployment Architecture
- Scalability & Performance
- Security Architecture
- Monitoring & Observability
- Migration Strategy
- Technology Stack
1. Executive Summary¶
1.1 Purpose¶
This document outlines the advanced microservices architecture for DSTA (Dr. Strange Trading Analysis), transforming it from a monolithic application into a scalable, resilient, distributed system capable of handling high-frequency trading operations across multiple cryptocurrency exchanges.
1.2 Key Objectives¶
- Scalability: Support 1000+ concurrent trading pairs and 100+ bot instances
- Resilience: Achieve 99.99% uptime with fault isolation and graceful degradation
- Performance: Sub-100ms latency for critical trading paths
- Maintainability: Enable independent deployment and development of services
- Extensibility: Easy integration of new exchanges, strategies, and data sources
1.3 Architectural Principles¶
- Domain-Driven Design: Services organized around business capabilities
- Event-Driven Architecture: Asynchronous communication for loose coupling
- API Gateway Pattern: Unified entry point with rate limiting and authentication
- Database per Service: Each service owns its data store
- CQRS: Separate read and write models for optimal performance
- Circuit Breaker: Fault tolerance and cascading failure prevention
- Observability: Comprehensive logging, metrics, and distributed tracing
2. Current State Analysis¶
2.1 Existing Architecture¶
The current DSTA system is a semi-monolithic Django application with the following components:
Current Architecture:
┌─────────────────────────────────────────────┐
│ Django Monolith │
├─────────────────────────────────────────────┤
│ - API Server (REST + WebSocket) │
│ - Data Collection (Exchange Clients) │
│ - Bot Logic │
│ - Telegram Integration │
│ - Background Tasks │
└─────────────────────────────────────────────┘
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│PostgreSQL│ │ Redis │
└──────────┘ └──────────┘
2.2 Pain Points¶
- Scalability Bottlenecks
- Single database limits horizontal scaling
- Cannot scale individual components independently
-
Resource contention between data collection and trading logic
-
Deployment Challenges
- All-or-nothing deployments increase risk
- Difficult to rollback specific features
-
Long deployment windows affect trading operations
-
Fault Isolation
- Exchange API failures can crash entire application
- One slow query can impact all operations
-
Limited ability to isolate misbehaving components
-
Development Velocity
- Large codebase slows development
- Tight coupling between modules
- Difficult to onboard new developers
2.3 Migration Drivers¶
- Business Growth: Need to support 10x more trading volume
- Multi-Region Deployment: Latency reduction for global exchanges
- Advanced Features: ML model serving, complex event processing
- Team Scaling: Enable parallel development by multiple teams
3. Microservices Architecture Overview¶
3.1 High-Level Architecture¶
┌─────────────────────────────────────────────────────────────────────────┐
│ Client Applications │
│ (Web Dashboard, Mobile App, CLI Tools, Third-Party Integrations) │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ API Gateway (Kong/Traefik) │
│ Authentication │ Rate Limiting │ Request Routing │ Load Balancing │
└─────────────────────────────────────────────────────────────────────────┘
│
┌───────────────────────────┼───────────────────────────┐
│ │ │
▼ ▼ ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Core Services │ │ Trading Engine │ │ Data Services │
├──────────────────┤ ├──────────────────┤ ├──────────────────┤
│ • User Service │ │ • Strategy Svc │ │ • Market Data │
│ • Auth Service │ │ • Order Exec Svc │ │ • Historical DB │
│ • Config Service │ │ • Position Mgmt │ │ • Analytics Svc │
│ • Notification │ │ • Risk Mgmt Svc │ │ • ML Model Svc │
└──────────────────┘ └──────────────────┘ └──────────────────┘
│ │ │
└───────────────────────────┼───────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Event Streaming Platform │
│ (Apache Kafka / Redis Streams) │
│ • Trade Events │ Market Data │ System Events │ Audit Logs │
└─────────────────────────────────────────────────────────────────────────┘
│
┌───────────────────────────┼───────────────────────────┐
│ │ │
▼ ▼ ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Exchange Gateway │ │ Backtest Engine │ │ Support Services │
├──────────────────┤ ├──────────────────┤ ├──────────────────┤
│ • Binance Svc │ │ • Simulation Svc │ │ • Logging Aggr │
│ • Huobi Service │ │ • Performance │ │ • Metrics Coll │
│ • Gate.io Svc │ │ • Optimization │ │ • Health Check │
│ • WebSocket Hub │ │ • Reporting Svc │ │ • Service Disc │
└──────────────────┘ └──────────────────┘ └──────────────────┘
│ │ │
└───────────────────────────┼───────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Data Layer │
├────────────┬────────────┬────────────┬────────────┬─────────────────────┤
│ PostgreSQL │ MongoDB │TimescaleDB │ Redis │ Object Storage │
│(Relational)│ (Documents)│(Time-Series)│ (Cache) │ (S3/MinIO) │
└────────────┴────────────┴────────────┴────────────┴─────────────────────┘
3.2 Service Boundaries¶
Services are organized into six domain groups:
- Core Services: Authentication, user management, configuration
- Trading Engine: Strategy execution, order management, risk control
- Data Services: Market data ingestion, historical storage, analytics
- Exchange Gateway: Exchange-specific adapters and connectors
- Backtesting: Strategy simulation and optimization
- Support Services: Observability, monitoring, service mesh
4. Service Catalog¶
4.1 Core Services Domain¶
4.1.1 User Service¶
Responsibility: User account management and profile data
Capabilities: - User registration and authentication - Profile management - API key management for users - User preferences and settings
Technology: Python (FastAPI), PostgreSQL
Scaling: Horizontal (stateless)
Data: User profiles, credentials (encrypted)
4.1.2 Auth Service¶
Responsibility: Authentication and authorization
Capabilities: - JWT token generation and validation - OAuth2 integration - Role-based access control (RBAC) - API key authentication - Session management
Technology: Python (FastAPI), Redis
Scaling: Horizontal (stateless)
Data: Active sessions, refresh tokens
4.1.3 Configuration Service¶
Responsibility: Centralized configuration management
Capabilities: - Exchange credentials storage (encrypted) - Strategy configuration versioning - Feature flags - Environment-specific settings - Configuration hot-reload
Technology: Python (FastAPI), PostgreSQL, Redis
Scaling: Horizontal with cache
Data: Configuration schemas, encrypted secrets
4.1.4 Notification Service¶
Responsibility: Multi-channel notifications
Capabilities: - Telegram bot integration - Email notifications - Webhook delivery - Alert prioritization and deduplication - Notification templates
Technology: Python (FastAPI), Redis (queue)
Scaling: Horizontal (queue-based)
Data: Notification logs, delivery status
4.2 Trading Engine Domain¶
4.2.1 Strategy Service¶
Responsibility: Strategy lifecycle management
Capabilities: - Strategy registration and versioning - Strategy parameter management - Strategy scheduling and triggers - Signal generation coordination - Strategy performance tracking
Technology: Python (FastAPI), PostgreSQL, Redis
Scaling: Horizontal with sharding by strategy
Data: Strategy definitions, parameters, state
4.2.2 Order Execution Service¶
Responsibility: Order lifecycle management
Capabilities: - Order creation and submission - Order status tracking - Partial fill handling - Order amendment and cancellation - Smart order routing - Execution algorithms (TWAP, VWAP)
Technology: Python (FastAPI), PostgreSQL, Redis
Scaling: Vertical + horizontal (critical path)
Data: Orders, fills, execution reports
4.2.3 Position Management Service¶
Responsibility: Portfolio and position tracking
Capabilities: - Real-time position calculation - P&L tracking (realized and unrealized) - Portfolio aggregation - Position reconciliation - Margin calculation
Technology: Python (FastAPI), PostgreSQL, Redis
Scaling: Horizontal with sticky sessions
Data: Positions, portfolio snapshots
4.2.4 Risk Management Service¶
Responsibility: Pre-trade and post-trade risk controls
Capabilities: - Position limit checks - Drawdown monitoring - Value at Risk (VaR) calculation - Risk budget allocation - Circuit breakers - Emergency stop mechanisms
Technology: Python (FastAPI), Redis (real-time rules)
Scaling: Vertical + horizontal with replication
Data: Risk limits, breach history
4.3 Data Services Domain¶
4.3.1 Market Data Service¶
Responsibility: Real-time and historical market data
Capabilities: - Market data ingestion from exchanges - Real-time price distribution - Order book management - Market data normalization - Data quality validation - Historical data API
Technology: Python (FastAPI), TimescaleDB, Redis
Scaling: Horizontal by exchange/symbol
Data: Candlesticks, order books, trades
4.3.2 Historical Data Service¶
Responsibility: Long-term data storage and retrieval
Capabilities: - Time-series data storage - Data compression and archival - Efficient range queries - Data export (CSV, Parquet) - Corporate action adjustments - Data gap filling
Technology: Python (FastAPI), TimescaleDB, S3
Scaling: Horizontal by time range
Data: Historical OHLCV, tick data
4.3.3 Analytics Service¶
Responsibility: Market analytics and insights
Capabilities: - Technical indicator calculation - Correlation analysis - Market regime detection - Sentiment analysis - Performance metrics calculation - Custom analytics pipelines
Technology: Python (FastAPI), Redis (cache), Pandas
Scaling: Horizontal (compute-intensive)
Data: Calculated indicators, analytics results
4.3.4 ML Model Service¶
Responsibility: Machine learning model serving
Capabilities: - Model versioning and registry - Real-time inference - Batch prediction - Feature engineering - Model monitoring and drift detection - A/B testing framework
Technology: Python (FastAPI), TensorFlow Serving, MLflow
Scaling: Horizontal with GPU support
Data: Model artifacts, predictions, features
4.4 Exchange Gateway Domain¶
4.4.1 Binance Service¶
Responsibility: Binance exchange integration
Capabilities: - REST API client - WebSocket connection management - Order submission and tracking - Account data retrieval - Rate limit management - API key rotation
Technology: Python (FastAPI), Redis
Scaling: Horizontal with connection pooling
Data: API credentials, connection state
4.4.2 Huobi Service¶
Responsibility: Huobi exchange integration
Capabilities: (Same as Binance Service)
Technology: Python (FastAPI), Redis
Scaling: Horizontal with connection pooling
Data: API credentials, connection state
4.4.3 Gate.io Service¶
Responsibility: Gate.io exchange integration
Capabilities: (Same as Binance Service)
Technology: Python (FastAPI), Redis
Scaling: Horizontal with connection pooling
Data: API credentials, connection state
4.4.4 WebSocket Hub Service¶
Responsibility: Centralized WebSocket connection management
Capabilities: - Connection pooling and reuse - Automatic reconnection - Message fan-out to subscribers - Connection health monitoring - Bandwidth optimization
Technology: Python (FastAPI/Channels), Redis
Scaling: Horizontal with sticky sessions
Data: Active connections, subscriptions
4.5 Backtesting Domain¶
4.5.1 Simulation Service¶
Responsibility: Strategy backtesting execution
Capabilities: - Event-driven simulation engine - Market replay from historical data - Realistic order execution simulation - Slippage and fee modeling - Multi-strategy backtests - Walk-forward analysis
Technology: Python (FastAPI), TimescaleDB
Scaling: Horizontal (CPU-intensive)
Data: Backtest configurations, results
4.5.2 Performance Analysis Service¶
Responsibility: Backtest result analysis
Capabilities: - Risk-adjusted metrics calculation - Drawdown analysis - Trade statistics - Equity curve generation - Comparative analysis - Report generation (PDF/HTML)
Technology: Python (FastAPI), PostgreSQL
Scaling: Horizontal (compute-intensive)
Data: Performance metrics, reports
4.5.3 Optimization Service¶
Responsibility: Strategy parameter optimization
Capabilities: - Grid search optimization - Genetic algorithm optimization - Bayesian optimization - Walk-forward optimization - Multi-objective optimization - Overfitting detection
Technology: Python (FastAPI), Redis (job queue)
Scaling: Horizontal with job distribution
Data: Optimization jobs, results
4.5.4 Reporting Service¶
Responsibility: Backtest visualization and reporting
Capabilities: - Interactive charts generation - PDF report creation - Email report delivery - Performance dashboard - Comparative reports - Custom report templates
Technology: Python (FastAPI), Object Storage
Scaling: Horizontal
Data: Generated reports, templates
4.6 Support Services Domain¶
4.6.1 Logging Aggregation Service¶
Responsibility: Centralized log collection and search
Capabilities: - Log collection from all services - Log parsing and indexing - Full-text search - Log retention policies - Log-based alerts
Technology: ELK Stack (Elasticsearch, Logstash, Kibana)
Scaling: Horizontal (Elasticsearch cluster)
Data: Application logs, audit trails
4.6.2 Metrics Collection Service¶
Responsibility: System and business metrics
Capabilities: - Metrics collection (Prometheus) - Time-series storage - Alerting rules - Grafana dashboards - SLA monitoring
Technology: Prometheus, Grafana, AlertManager
Scaling: Horizontal with federation
Data: Metrics time-series
4.6.3 Distributed Tracing Service¶
Responsibility: Request tracing across services
Capabilities: - Trace collection - Span visualization - Latency analysis - Dependency mapping - Performance bottleneck detection
Technology: Jaeger / OpenTelemetry
Scaling: Horizontal
Data: Trace spans, service maps
4.6.4 Service Discovery¶
Responsibility: Dynamic service registration and discovery
Capabilities: - Service registration - Health checking - Service lookup - Load balancing - Failover management
Technology: Consul / Eureka
Scaling: Horizontal with quorum
Data: Service registry, health status
5. Data Architecture¶
5.1 Database Strategy¶
5.1.1 Database per Service Pattern¶
Each service owns its database, ensuring: - Data Encapsulation: Services expose data through APIs only - Independent Scaling: Optimize each database for its workload - Technology Freedom: Choose the best database for each use case - Fault Isolation: Database failures don't cascade
5.1.2 Database Types and Usage¶
| Service Domain | Database Type | Technology | Rationale |
|---|---|---|---|
| User/Auth | Relational | PostgreSQL | ACID compliance, referential integrity |
| Configuration | Relational | PostgreSQL | Strong consistency, versioning |
| Orders/Positions | Relational | PostgreSQL | Transactional integrity |
| Market Data | Time-Series | TimescaleDB | Optimized for time-series queries |
| Strategy State | Key-Value | Redis | Low-latency read/write |
| Analytics Cache | Document | MongoDB | Flexible schema, aggregations |
| ML Features | Columnar | Parquet/S3 | Analytics, batch processing |
| Logs | Document | Elasticsearch | Full-text search, indexing |
5.2 Data Consistency Patterns¶
5.2.1 Event Sourcing¶
Use Case: Order lifecycle, position changes
Store all changes as immutable events:
Benefits: - Complete audit trail - Easy to replay and rebuild state - Temporal queries (state at any point in time)
5.2.2 CQRS (Command Query Responsibility Segregation)¶
Use Case: Market data, analytics
Separate write and read models: - Write Model: Optimized for data ingestion (TimescaleDB) - Read Model: Optimized for queries (Redis, MongoDB)
5.2.3 Saga Pattern¶
Use Case: Distributed transactions (order placement across multiple exchanges)
Coordinate long-running transactions:
5.3 Data Synchronization¶
5.3.1 Change Data Capture (CDC)¶
Use Debezium to capture database changes and publish to Kafka: - Real-time data replication - Materialized view updates - Cache invalidation
5.3.2 ETL Pipelines¶
Batch data synchronization for analytics: - Hourly aggregations (1h → 4h → 1d) - Daily reporting pipelines - Weekly model retraining
6. Communication Patterns¶
6.1 Synchronous Communication (REST/gRPC)¶
6.1.1 When to Use¶
- Read operations (queries)
- Command operations requiring immediate response
- Low-latency requirements (< 100ms)
6.1.2 API Gateway Pattern¶
All external requests go through API Gateway (Kong/Traefik):
Gateway Responsibilities: - Authentication/Authorization - Rate limiting - Request/Response transformation - Circuit breaking - Caching
6.1.3 Service-to-Service Communication¶
Use gRPC for internal service communication: - Performance: Binary protocol, HTTP/2 - Type Safety: Protocol Buffers - Streaming: Bidirectional streaming support
6.2 Asynchronous Communication (Events)¶
6.2.1 Event Streaming Platform¶
Use Apache Kafka / Redis Streams:
Kafka Topics: - market-data.prices - Real-time price updates - market-data.orderbooks - Order book snapshots - trading.orders - Order lifecycle events - trading.positions - Position changes - risk.alerts - Risk management alerts - system.events - System-wide events - audit.logs - Audit trail
6.2.2 Event-Driven Patterns¶
Publish-Subscribe:
Market Data Service → [market-data.prices] → Strategy Service
→ Analytics Service
→ ML Model Service
Event Sourcing:
CQRS Read Model Updates:
6.3 Message Queue Patterns¶
Use Redis Streams for task queues: - Notification Queue: Email, Telegram, Webhook delivery - Report Generation: Async report creation - Batch Jobs: Data archival, aggregations
7. Deployment Architecture¶
7.1 Container Orchestration¶
7.1.1 Kubernetes Architecture¶
┌─────────────────────────────────────────────────────────────────┐
│ Kubernetes Cluster │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐│
│ │ Namespace: │ │ Namespace: │ │ Namespace: ││
│ │ Production │ │ Staging │ │ Development ││
│ └─────────────────┘ └─────────────────┘ └─────────────────┘│
│ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Ingress Controller (Nginx/Traefik) │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
│ ┌───────────┬───────────┬───────────┬───────────┬──────────┐ │
│ │ Service │ Service │ Service │ Service │ Service │ │
│ │ Pod(s) │ Pod(s) │ Pod(s) │ Pod(s) │ Pod(s) │ │
│ └───────────┴───────────┴───────────┴───────────┴──────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ ConfigMaps & Secrets (Configuration) │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Persistent Volumes (StatefulSets for Databases) │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
7.1.2 Service Deployment Specifications¶
Stateless Services (API, Trading Logic): - Deployment: ReplicaSet with horizontal autoscaling - Replicas: Min 2, Max 10 (based on CPU/memory) - Resources: 500m CPU, 512Mi memory (requests/limits) - Probes: Liveness, Readiness, Startup
Stateful Services (Databases): - Deployment: StatefulSet with persistent volumes - Replicas: 3 (Primary + 2 Replicas) - Resources: 2 CPU, 4Gi memory - Storage: SSD-backed persistent volumes
7.2 Multi-Region Deployment¶
7.2.1 Regional Architecture¶
┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐
│ Region: US │ │ Region: EU │ │ Region: APAC │
├───────────────────┤ ├───────────────────┤ ├───────────────────┤
│ • Full Services │ │ • Full Services │ │ • Full Services │
│ • Local Database │ │ • Local Database │ │ • Local Database │
│ • Kafka Cluster │ │ • Kafka Cluster │ │ • Kafka Cluster │
└───────────────────┘ └───────────────────┘ └───────────────────┘
│ │ │
└────────────────────────┼────────────────────────┘
│
Cross-Region Sync
(Kafka MirrorMaker)
Regional Routing: - DNS-based routing to nearest region - Active-Active for trading services - Asynchronous replication for analytics data
7.3 CI/CD Pipeline¶
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Git Push │───▶│ CI Build │───▶│ Tests │───▶│ Deploy Dev │
└──────────────┘ │ (GitHub │ │ (Unit, │ └──────────────┘
│ Actions) │ │ Integration)│ │
└──────────────┘ └──────────────┘ ▼
┌──────────────┐
│ Deploy Stage │
└──────────────┘
│
▼
┌──────────────┐
│ Manual │
│ Approval │
└──────────────┘
│
▼
┌──────────────┐
│ Deploy Prod │
│ (Blue/Green) │
└──────────────┘
Deployment Strategies: - Blue/Green: Zero-downtime deployments - Canary: Gradual rollout with monitoring - Rolling Update: Default for non-critical services
8. Scalability & Performance¶
8.1 Scaling Strategies¶
8.1.1 Horizontal Scaling¶
Stateless Services: - Auto-scale based on CPU (> 70%) or Request Rate (> 1000 req/s) - Use Kubernetes HPA (Horizontal Pod Autoscaler) - Load balance with round-robin or least-connections
Stateful Services: - Sharding by key (exchange, symbol, time range) - Read replicas for query scaling - Write scaling through partitioning
8.1.2 Vertical Scaling¶
When to Use: - Latency-sensitive services (Order Execution) - Services with memory-intensive operations (ML Model Serving)
Limits: - Max 16 CPU, 64GB memory per pod - Use vertical scaling as temporary solution before horizontal
8.1.3 Data Layer Scaling¶
PostgreSQL: - Read Scaling: Read replicas (up to 5) - Write Scaling: Partitioning by time/symbol - Connection Pooling: PgBouncer (500 connections per pool)
TimescaleDB: - Automatic Partitioning: Hypertables - Compression: Older data compressed (10:1 ratio) - Distributed Hypertables: Shard across multiple nodes
Redis: - Redis Cluster: 6 nodes (3 primary, 3 replica) - Sentinel: Automatic failover - Partitioning: Consistent hashing
Kafka: - Partitions: 10-30 per topic for parallelism - Replication Factor: 3 for durability - Consumer Groups: Scale consumers independently
8.2 Performance Optimization¶
8.2.1 Caching Strategy¶
Multi-Level Caching:
Cache Patterns: - Cache-Aside: Application manages cache - Write-Through: Update cache on write - Write-Behind: Async cache update
Cache Invalidation: - TTL-based (5 minutes for market data) - Event-based (on order execution) - Version-based (configuration changes)
8.2.2 Database Optimization¶
Indexing: - B-Tree: Primary keys, foreign keys - Composite: (exchange, symbol, timestamp) - Partial: (WHERE status = 'active')
Query Optimization: - Connection pooling (reduce connection overhead) - Prepared statements (reduce parsing) - Batch operations (reduce round trips) - Materialized views (pre-computed aggregations)
8.2.3 API Performance¶
Response Time Targets: - Market data retrieval: < 50ms (p99) - Order submission: < 100ms (p99) - Portfolio query: < 200ms (p99) - Backtest submission: < 500ms (p99)
Optimization Techniques: - Response compression (gzip, brotli) - Pagination (limit 100 records per page) - Field filtering (return only requested fields) - Async processing for long-running tasks
9. Security Architecture¶
9.1 Defense in Depth¶
┌─────────────────────────────────────────────────────────────────┐
│ Layer 1: Network Security │
│ • WAF (Web Application Firewall) │
│ • DDoS Protection │
│ • VPN/Private Network │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Layer 2: API Gateway Security │
│ • Rate Limiting (100 req/min per IP) │
│ • IP Whitelisting │
│ • JWT Validation │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Layer 3: Service Security │
│ • Service-to-Service mTLS │
│ • RBAC (Role-Based Access Control) │
│ • Input Validation & Sanitization │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Layer 4: Data Security │
│ • Encryption at Rest (AES-256) │
│ • Encryption in Transit (TLS 1.3) │
│ • Secret Management (Vault/Kubernetes Secrets) │
└─────────────────────────────────────────────────────────────────┘
9.2 Authentication & Authorization¶
9.2.1 User Authentication¶
- JWT Tokens: 15-minute access token, 7-day refresh token
- OAuth2: Support for third-party integrations
- API Keys: For programmatic access (rotated every 90 days)
- MFA: Optional two-factor authentication
9.2.2 Service-to-Service Authentication¶
- Mutual TLS (mTLS): Certificate-based authentication
- Service Accounts: Kubernetes service accounts with RBAC
- API Gateway: Centralized authentication enforcement
9.2.3 Authorization Model¶
Roles: - Admin: Full system access - Trader: Trading operations, strategy management - Analyst: Read-only access to data and analytics - Bot: Automated trading operations (limited scope)
Permissions: - Fine-grained permissions (e.g., order:create, strategy:read) - Resource-level permissions (e.g., access to specific exchanges)
9.3 Secret Management¶
HashiCorp Vault or Kubernetes Secrets: - Exchange API keys (encrypted at rest) - Database credentials (auto-rotated) - Service certificates (auto-renewed) - Encryption keys (multi-key rotation)
Secret Rotation: - API Keys: Every 90 days (automated) - Database Passwords: Every 180 days - TLS Certificates: Every 365 days (Let's Encrypt)
9.4 Audit & Compliance¶
Audit Logging: - All trading operations (immutable log) - User actions (login, config changes) - System events (deployments, failures)
Compliance: - GDPR: User data retention policies - SOC 2: Access controls, encryption - PCI DSS: If handling payment data
10. Monitoring & Observability¶
10.1 Monitoring Stack¶
┌─────────────────────────────────────────────────────────────────┐
│ Observability Platform │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Metrics │ │ Logs │ │ Traces │ │
│ │ (Prometheus) │ │(Elasticsearch)│ │ (Jaeger) │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ └──────────────────┼──────────────────┘ │
│ │ │
│ ┌───────▼────────┐ │
│ │ Visualization │ │
│ │ (Grafana) │ │
│ └────────────────┘ │
│ │ │
│ ┌───────▼────────┐ │
│ │ Alerting │ │
│ │ (AlertManager) │ │
│ └────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
10.2 Key Metrics¶
10.2.1 Business Metrics¶
- Trading Volume: Total volume traded per hour/day
- P&L: Realized and unrealized profit/loss
- Win Rate: Percentage of profitable trades
- Sharpe Ratio: Risk-adjusted returns
- Order Fill Rate: Percentage of orders successfully filled
- Slippage: Difference between expected and actual execution price
10.2.2 System Metrics¶
- Latency: Request/response time (p50, p95, p99)
- Throughput: Requests per second
- Error Rate: 4xx and 5xx responses
- Availability: Uptime percentage (target: 99.99%)
- Resource Utilization: CPU, memory, disk, network
10.2.3 Service Metrics¶
- Order Execution Service:
- Orders per second
- Order placement latency (< 100ms p99)
- Order rejection rate
-
Partial fill rate
-
Market Data Service:
- Data ingestion rate (ticks/sec)
- WebSocket connection count
- Data latency (exchange → service)
-
Data gap detection
-
Strategy Service:
- Active strategies count
- Signal generation rate
- Strategy execution time
- Strategy error rate
10.3 Alerting¶
10.3.1 Alert Levels¶
Critical (Page On-Call): - Service down (> 5 minutes) - Database connection failure - Exchange API connection lost - Risk limit breached - Unusual trading activity
Warning (Slack Notification): - High latency (> 500ms p99) - Error rate spike (> 5%) - Approaching risk limits (> 80%) - Low disk space (< 20%)
Info (Dashboard Only): - Configuration changes - Deployment events - Scheduled maintenance
10.3.2 Alert Routing¶
Alert → AlertManager → Routing Rules
│
┌─────────────────┼─────────────────┐
▼ ▼ ▼
PagerDuty Slack Email
(Critical) (Warning) (Info)
10.4 Distributed Tracing¶
Trace Context Propagation:
API Gateway → Strategy Service → Order Execution → Exchange Gateway
│ │ │ │
└──────────────┴───────────────────┴──────────────────┘
│
Trace Collector
│
Jaeger Backend
Tracing Use Cases: - Identify bottlenecks in request path - Debug cross-service failures - Measure end-to-end latency - Service dependency visualization
11. Migration Strategy¶
11.1 Strangler Fig Pattern¶
Gradually replace monolith with microservices:
Phase 1: Extract Exchange Gateways
Monolith → [API Gateway] → Exchange Services (New)
↘ Monolith (Old)
Phase 2: Extract Trading Engine
Monolith → [API Gateway] → Trading Services (New)
↘ Monolith (Old)
Phase 3: Extract Data Services
Monolith → [API Gateway] → Data Services (New)
↘ Monolith (Old)
Phase 4: Decompose Remaining Monolith
[API Gateway] → All Microservices (New)
Monolith retired
11.2 Migration Phases¶
Phase 1: Foundation (Weeks 1-4)¶
- Set up Kubernetes cluster
- Deploy API Gateway
- Set up monitoring stack
- Implement service mesh (Istio)
- Establish CI/CD pipelines
Deliverables: - Kubernetes cluster with 3 nodes - Prometheus + Grafana dashboards - API Gateway with basic routing
Phase 2: Extract Exchange Gateways (Weeks 5-8)¶
- Create Exchange Gateway services (Binance, Huobi, Gate.io)
- Migrate WebSocket connections
- Set up Kafka for market data streaming
- Dual-write to both old and new systems
Deliverables: - 3 Exchange Gateway microservices - Kafka cluster with market data topics - WebSocket Hub service
Phase 3: Extract Data Services (Weeks 9-12)¶
- Create Market Data Service
- Create Historical Data Service
- Set up TimescaleDB
- Migrate historical data
- Implement CDC for real-time sync
Deliverables: - Market Data and Historical Data services - TimescaleDB with 2 years of historical data - CDC pipeline for data synchronization
Phase 4: Extract Trading Engine (Weeks 13-16)¶
- Create Strategy Service
- Create Order Execution Service
- Create Position Management Service
- Create Risk Management Service
- Migrate active strategies
Deliverables: - 4 Trading Engine microservices - Active trading strategies running on new services - Real-time position tracking
Phase 5: Extract Core Services (Weeks 17-20)¶
- Create User Service
- Create Auth Service
- Create Configuration Service
- Create Notification Service
- Migrate user authentication
Deliverables: - 4 Core microservices - JWT-based authentication - Centralized configuration management
Phase 6: Backtesting & Analytics (Weeks 21-24)¶
- Create Simulation Service
- Create Analytics Service
- Create ML Model Service
- Migrate backtesting workflows
Deliverables: - Complete backtesting platform - ML model serving infrastructure - Analytics dashboards
Phase 7: Decommission Monolith (Weeks 25-28)¶
- Verify all functionality migrated
- Run shadow mode (parallel systems)
- Gradual traffic shift (10% → 50% → 100%)
- Decommission monolith
Deliverables: - 100% traffic on microservices - Monolith shut down - Complete migration documentation
11.3 Risk Mitigation¶
Rollback Plan: - Keep monolith running in parallel for 4 weeks - Feature flags to toggle between old/new systems - Database snapshots before each migration phase - Automated rollback scripts
Testing Strategy: - Integration tests for each new service - End-to-end tests for critical paths - Load testing to validate performance - Chaos engineering (failure injection)
Communication Plan: - Weekly migration updates - Runbooks for on-call engineers - User communication for downtime windows - Stakeholder demos at each phase
12. Technology Stack¶
12.1 Services Layer¶
| Component | Technology | Rationale |
|---|---|---|
| API Framework | FastAPI | High performance, async support, OpenAPI |
| API Gateway | Kong / Traefik | Feature-rich, scalable, plugin ecosystem |
| gRPC | gRPC Python | Efficient service-to-service communication |
| WebSocket | FastAPI Channels | Real-time data streaming |
12.2 Data Layer¶
| Component | Technology | Rationale |
|---|---|---|
| Relational DB | PostgreSQL 17 | ACID, mature, excellent performance |
| Time-Series DB | TimescaleDB | PostgreSQL extension, optimized for time-series |
| Cache | Redis 8 | In-memory, pub/sub, streams |
| Document DB | MongoDB | Flexible schema, aggregations |
| Object Storage | MinIO / S3 | Scalable, cost-effective |
| Search | Elasticsearch | Full-text search, log aggregation |
12.3 Messaging & Streaming¶
| Component | Technology | Rationale |
|---|---|---|
| Event Streaming | Apache Kafka | High throughput, durability, replayability |
| Message Queue | Redis Streams | Lightweight, integrated with cache |
| CDC | Debezium | Reliable change data capture |
12.4 Orchestration & Infrastructure¶
| Component | Technology | Rationale |
|---|---|---|
| Container Runtime | Docker | Industry standard |
| Orchestration | Kubernetes | De-facto standard, rich ecosystem |
| Service Mesh | Istio | Traffic management, observability, security |
| CI/CD | GitHub Actions | Integrated with repo, flexible |
| IaC | Terraform | Multi-cloud, declarative |
12.5 Observability¶
| Component | Technology | Rationale |
|---|---|---|
| Metrics | Prometheus | Pull-based, PromQL, integrations |
| Logging | ELK Stack | Scalable, searchable, visualizations |
| Tracing | Jaeger | OpenTelemetry compatible |
| Visualization | Grafana | Unified dashboards, alerting |
| APM | Datadog / NewRelic | (Optional) All-in-one solution |
12.6 Security¶
| Component | Technology | Rationale |
|---|---|---|
| Secret Management | Vault / K8s Secrets | Encryption, rotation, audit |
| Certificate Manager | cert-manager | Automated TLS certificate management |
| WAF | ModSecurity | OWASP rules, SQL injection protection |
| Identity | Keycloak | SSO, OAuth2, OIDC |
13. Appendices¶
Appendix A: Service API Specifications¶
Detailed API specifications for each service are maintained in: - /docs/api/ - OpenAPI 3.0 specifications - /docs/grpc/ - Protocol Buffer definitions
Appendix B: Database Schemas¶
Database schema documentation: - /docs/schemas/postgresql/ - PostgreSQL table definitions - /docs/schemas/timescale/ - TimescaleDB hypertable schemas - /docs/schemas/redis/ - Redis data structure designs
Appendix C: Deployment Runbooks¶
Operational runbooks: - /docs/runbooks/deployment.md - Deployment procedures - /docs/runbooks/incident-response.md - Incident handling - /docs/runbooks/disaster-recovery.md - DR procedures
Appendix D: Performance Benchmarks¶
Performance testing results: - /docs/benchmarks/ - Load test results and analysis
Conclusion¶
This microservices architecture provides DSTA with a scalable, resilient, and maintainable foundation for growth. The phased migration approach minimizes risk while delivering incremental value. With proper implementation, DSTA will be capable of:
- 10x Scale: Support 1000+ trading pairs and 100+ bot instances
- 99.99% Uptime: High availability with fault isolation
- Sub-100ms Latency: Critical trading path optimization
- Multi-Region: Global deployment for latency reduction
- Rapid Development: Independent service development and deployment
The journey from monolith to microservices is significant but achievable with disciplined execution, comprehensive testing, and incremental rollout.
Document Status: Architecture Proposal
Next Steps: 1. Review and approval by technical leadership 2. Proof of concept for critical services (Exchange Gateway, Order Execution) 3. Finalize technology selections 4. Begin Phase 1 implementation
Maintainers: DSTA Architecture Team
Last Reviewed: 2026-01-16