Cloud Adoption Guide for Financial Market Infrastructure

# Multi-Site Active/Active Trading Architecture

PRIMARY TRADING INFRASTRUCTURE
===============================

AWS Region A (Active)          AWS Region B (Active)
       |                              |
       +-------- Sync Replication ----+
       |                              |
  AWS Outposts                   AWS Outposts
  (bmsfe-2)                      (bmsfe-2)
       |                              |
  Matching Engine               Matching Engine
  (under 20 microsec)           (under 20 microsec)


TERTIARY (DR) ENVIRONMENT
=========================
Disconnected Outposts Rack
(5-Day Autonomous Operations for Regulatory Compliance)

- Static stability: No dependency on AWS control plane
- Local state persistence for full market operations
- Automatic failover with order book reconciliation


Performance Metrics:
- Matching Engine Latency: 2x faster than on-premises at all percentiles
- Median Latency: 6x improvement vs. legacy infrastructure
- Failover Time: Sub-second with zero order loss

# EBA-Driven Migration Approach for Clearing Systems

Phase 1: Foundation (3 months)
- Identity governance framework (AWS IAM Identity Center)
- Network architecture with Direct Connect
- Security baseline and compliance controls
- Monitoring and observability setup

Phase 2: Non-Critical Workloads (6 months)
- Reference data systems
- Reporting and analytics
- Development and testing environments
- Validate migration patterns

Phase 3: Mission-Critical Systems (12 months)
- Collateral Management System (CMS)
- Risk calculation engines
- Trade lifecycle processing
- Real-time margin calculation

Phase 4: Core Clearing (18 months)
- Order management systems
- Position keeping
- Settlement processing
- Regulatory reporting

# Key Metrics from Production Migrations:
# - 11 EBAs conducted across 13 cross-functional teams
# - 5 repeatable migration patterns identified
# - 26 process improvements documented
# - 50+ critical blockers removed
# - 12+ applications migrated over 3 years

# Market Data Platform Architecture

REAL-TIME DATA INGESTION
========================
575 Global Venues --> 5-10 TB/day --> 274+ Billion Messages/Day

Ingestion Services:
- Kinesis Data Streams
- Amazon MSK (Kafka)
- EventBridge (Events)


STORAGE LAYER - Amazon S3 (75+ Petabytes)
==========================================
- Raw PCAP Data (Network Captures)
- Normalized Tick Data (10 Years History)
- Pre-Calculated Analytics (Time Bars, VWAP)
- 30+ Million Instruments

Storage Optimization:
- S3 Intelligent-Tiering: $3.5M/year savings
- S3 Glacier for cold data
- Up to 80% storage cost reduction


ANALYTICS and DISTRIBUTION
==========================
- Athena (Query-in-Place)
- Redshift (Data Warehouse)
- S3 Direct Access (No data transfer, query where stored)

Delivery SLAs:
- Data available within 2 hours of market close
- Large datasets delivered in hours (previously days)
- Time to signal: 30-45 minutes (previously 3 days)

Cost Optimization Results:
- Storage: 97% reduction
- Compute: 82% reduction

# GenAI-Powered Market Surveillance System
import boto3
import json

bedrock = boto3.client('bedrock-runtime')

def analyze_news_sensitivity(news_article: dict) -> dict:
    """
    Analyze if news article is price-sensitive for market surveillance.
    Achieved: 100% accuracy for non-sensitive, 100% recall for price-sensitive.
    """
    
    prompt = f"""
    Analyze the following news article for price sensitivity.
    
    Article:
    Headline: {news_article['headline']}
    Content: {news_article['content']}
    Related Securities: {news_article['tickers']}
    
    Determine:
    1. Is this news price-sensitive? (YES/NO)
    2. Confidence score (0-100)
    3. Justification for the classification
    4. Affected securities and expected impact direction
    
    Respond in JSON format.
    """
    
    response = bedrock.invoke_model(
        modelId='anthropic.claude-3-sonnet-20240229-v1:0',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 1024,
            'messages': [{'role': 'user', 'content': prompt}]
        })
    )
    
    result = json.loads(response['body'].read())
    return json.loads(result['content'][0]['text'])

# Example output:
# {
#   "price_sensitive": true,
#   "confidence": 95,
#   "justification": "M&A announcement directly impacts valuation",
#   "affected_securities": ["TICKER1", "TICKER2"],
#   "expected_impact": "positive"
# }

# Enterprise Identity Governance with AWS IAM Identity Center

# Step 1: Configure identity source (Azure AD, Okta, etc.)
aws sso-admin create-instance-access-control-attribute-configuration \
    --instance-arn arn:aws:sso:::instance/ssoins-xxx \
    --access-control-attributes '[
        {"Key": "department", "Value": {"Source": ["enterprise.department"]}},
        {"Key": "costCenter", "Value": {"Source": ["enterprise.costCenter"]}},
        {"Key": "clearingRole", "Value": {"Source": ["enterprise.jobRole"]}}
    ]'

# Step 2: Create permission sets for different roles
aws sso-admin create-permission-set \
    --instance-arn arn:aws:sso:::instance/ssoins-xxx \
    --name "ClearingOperator" \
    --session-duration "PT8H" \
    --description "Access for clearing operations team"

# Step 3: Attach managed policies with least privilege
aws sso-admin attach-managed-policy-to-permission-set \
    --instance-arn arn:aws:sso:::instance/ssoins-xxx \
    --permission-set-arn arn:aws:sso:::permissionSet/xxx \
    --managed-policy-arn arn:aws:iam::aws:policy/ReadOnlyAccess

# Benefits:
# - Reduced operational complexity
# - Strengthened security controls
# - Automated provisioning/deprovisioning
# - Audit trail for all access

# Weekend Migration Pattern for Oracle Databases

# Pre-Migration (Week before)
1. Set up AWS DMS replication instance
2. Create DMS endpoints for source and target
3. Run full load task in test mode
4. Validate row counts and data integrity

# Friday Evening (After Market Close)
5. Stop application writes to source database
6. Verify DMS CDC has zero lag
7. Perform final validation checks

aws dms describe-replication-tasks \
    --filters "Name=replication-task-id,Values=quote-db-migration" \
    --query 'ReplicationTasks[0].ReplicationTaskStats'

# Saturday (Cutover Window)
8. Update application connection strings
9. Run smoke tests against RDS

# Sunday (Validation)
10. Performance baseline comparison
11. Failover testing (minutes vs. days previously)
12. Rollback preparation (just in case)

# Monday (Go-Live)
13. Monitor application performance
14. Decommission source database after 30-day parallel run

# Results from Production Migration:
# - 3 Oracle databases (63 TB total)
# - 10 billion quotes/day ingested
# - Single weekend cutover
# - 2 days/month maintenance saved
# - 15% performance improvement
# - Failover: minutes vs. days