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Financial Analysis System Architecture 🏗️

From 100 to 100,000 reports/day with regulatory compliance

June 19, 2025
💹 Finance🏗️ Architecture📊 Real-Time🔒 Compliant

From market data to actionable intelligence.

Monday: 3 core prompts (data ingestion, analysis, report generation). Tuesday: automated pipeline code. Wednesday: team workflows (analysts, compliance, distribution). Thursday: complete technical architecture. Multi-agent system, ML pipelines, real-time data processing, and regulatory compliance for financial institutions processing 100K+ reports daily.

Key Assumptions

  • Monitor 50-10,000 securities (stocks, bonds, derivatives) across global markets
  • Real-time data feeds for critical assets (sub-second latency), batch for others (hourly)
  • Generate 100-100,000 reports/day (market summaries, risk assessments, trading signals)
  • Regulatory compliance: SEC (US), FINRA, MiFID II (EU), data residency requirements
  • Multi-tenant SaaS for enterprise clients with isolated data and custom models
  • Integration with Bloomberg Terminal, Reuters Eikon, internal trading systems, CRM

System Requirements

Functional

  • Ingest market data from 10+ sources (APIs, FTP, WebSocket streams)
  • Extract structured data (prices, volumes, news, sentiment) using LLMs
  • Generate analysis reports (technical, fundamental, sentiment) with custom templates
  • Distribute reports via email, Slack, Bloomberg Terminal, API webhooks
  • Support custom queries (ad-hoc analysis, backtesting, scenario modeling)
  • Version control for prompts, models, and report templates
  • Audit trail for all data access, model decisions, and report generation

Non-Functional (SLOs)

latency p95 ms5000
freshness min5
availability percent99.9
report generation sec30
data ingestion delay sec10

💰 Cost Targets: {"per_report_usd":0.15,"per_security_monitored_usd":2,"monthly_infra_startup_usd":500,"monthly_infra_enterprise_usd":5000}

Agent Layer

planner

L4

Decompose user request into subtasks, select tools, coordinate agents

🔧 Task decomposer, Agent selector, Resource estimator

⚡ Recovery: If task decomposition fails → fallback to manual queue, If agent unavailable → reassign to backup agent, Retry with exponential backoff (3x)

executor

L3

Execute primary workflow (data ingestion → analysis → report generation)

🔧 Data Ingestion Agent, Analysis Agent, Report Agent, Feature Store API, ML Inference API

⚡ Recovery: If data ingestion fails → use cached data + flag staleness, If analysis fails → retry with simplified parameters, If report generation fails → generate text-only fallback, Checkpoint progress every 5 steps

evaluator

L3

Validate outputs, quality checks, confidence scoring

🔧 Statistical validator, Logical consistency checker, Hallucination detector, Benchmark comparator

⚡ Recovery: If quality < 0.7 → flag for human review, If hallucination detected → block output, retry with safer prompt, If validation service down → use rule-based fallback

guardrail

L4

Policy checks, PII redaction, regulatory compliance, safety filters

🔧 PII detector (AWS Comprehend), Policy engine (OPA), Content filter, Audit logger

⚡ Recovery: If PII detection fails → block processing, alert security team, If policy violation → halt execution, log incident, Never fail open (default deny)

data_ingestion

L2

Fetch market data from 10+ sources, normalize, store in Feature Store

🔧 Bloomberg API client, Reuters API client, Alpha Vantage client, Feature Store API, Data validator

⚡ Recovery: If primary source fails → fallback to secondary source, If all sources fail → use cached data + staleness warning, If normalization fails → log raw data, skip feature derivation, Retry with jitter (avoid thundering herd)

analysis

L3

Run technical, fundamental, and sentiment analysis using ML models

🔧 Technical analysis library (TA-Lib), Sentiment model (fine-tuned FinBERT), Risk model (custom LSTM), LLM (GPT-4 for narrative generation)

⚡ Recovery: If ML model fails → fallback to rule-based analysis, If LLM fails → use template-based narrative, If partial failure → generate report with available data + disclaimers, Cache intermediate results

report

L2

Generate PDF reports with charts, tables, and narratives

🔧 PDF generator (WeasyPrint/Puppeteer), Chart library (Plotly/Matplotlib), Template engine (Jinja2), S3 uploader

⚡ Recovery: If PDF generation fails → generate HTML fallback, If chart rendering fails → use text tables, If upload fails → retry 3x, then email directly, Timeout after 30 seconds

ML Layer

Feature Store

Update: Real-time for critical (sub-second), Hourly for derived, Daily for historical

  • price_close (raw)
  • volume (raw)
  • rsi_14 (derived)
  • macd (derived)
  • sentiment_score (derived)
  • volatility_30d (derived)
  • correlation_spy (derived)

Model Registry

Strategy: Semantic versioning (major.minor.patch), A/B testing for new versions

  • sentiment_classifier
  • risk_predictor
  • signal_generator

Observability

Metrics

  • 📊 report_generation_latency_p95_ms
  • 📊 data_ingestion_success_rate
  • 📊 ml_inference_latency_p50_ms
  • 📊 agent_task_completion_rate
  • 📊 hallucination_detection_rate
  • 📊 cost_per_report_usd
  • 📊 api_error_rate
  • 📊 cache_hit_ratio

Dashboards

  • 📈 ops_dashboard
  • 📈 ml_dashboard
  • 📈 compliance_dashboard
  • 📈 cost_dashboard

Traces

✅ Enabled

Deployment Variants

🚀 Startup

Infrastructure:

  • AWS Lambda (serverless)
  • API Gateway
  • RDS PostgreSQL (single-AZ)
  • S3 (reports)
  • CloudWatch (logs)
  • OpenAI API (GPT-4)
  • SendGrid (email)

Single-tenant (one customer)

Managed services (no Kubernetes)

Auto-scaling (serverless)

Cost: $200-800/mo

Time to deploy: 1-2 weeks

Good for MVP, early customers

🏢 Enterprise

Infrastructure:

  • Kubernetes (EKS/GKE)
  • Multi-region deployment
  • Aurora Global Database
  • VPC isolation per customer
  • Private LLM endpoints (Azure OpenAI)
  • BYO KMS/HSM
  • SSO/SAML (Okta, Azure AD)
  • Audit trail (CloudTrail → S3)
  • Data residency (US, EU)

Multi-tenant (1000+ customers)

99.99% SLA

Custom ML models

Cost: $15K+/mo

Time to deploy: 2-3 months

SOC 2 Type II, ISO 27001

📈 Migration: Startup → Enterprise: (1) Migrate Lambda to EKS (containerize). (2) Upgrade RDS to Aurora Global. (3) Add VPC isolation per customer. (4) Deploy private LLM endpoints. (5) Implement SSO/SAML. (6) Add multi-region failover. (7) Certify SOC 2. Timeline: 3-6 months.

Risks & Mitigations

⚠️ Market data source failure (Bloomberg API down)

Medium (1-2x/year)

✓ Mitigation: Multi-source strategy (Bloomberg → Reuters → Alpha Vantage). Cached data (15 min stale). Automatic failover (10 sec). SLA: 99.5%.

⚠️ LLM hallucination (fake earnings, false news)

Medium (0.5% of reports)

✓ Mitigation: 4-layer validation (confidence, cross-reference, logic, human review). Detection rate: 99.8%. Human review: 3% of reports. Never publish unvalidated.

⚠️ Model drift (accuracy decay over time)

High (quarterly)

✓ Mitigation: Real-time drift detection (KS test, PSI). Alert if drift >0.3. Auto-retrain if accuracy drops >5%. Blue-green deployment (0 downtime).

⚠️ Regulatory compliance violation (SEC, FINRA)

Low (with controls)

✓ Mitigation: Guardrail Agent (policy checks). Audit trail (7 years). PII redaction. Compliance officer review. SOC 2 Type II certified.

⚠️ Cost overrun (LLM API costs)

Medium (spiky usage)

✓ Mitigation: Cost guardrails ($5K/day limit). Alert if >80% of budget. Cache LLM responses (30% cost reduction). Use cheaper models for non-critical tasks.

⚠️ Data breach (customer data leaked)

Low (with security)

✓ Mitigation: Encryption at rest (KMS) + in transit (TLS 1.3). RBAC (least privilege). WAF (block attacks). Penetration testing (quarterly). SOC 2 Type II.

⚠️ Vendor lock-in (AWS-specific services)

Medium

✓ Mitigation: Multi-cloud strategy (AWS primary, GCP backup). Use open-source tools (Kubernetes, PostgreSQL, Kafka). Abstract vendor-specific APIs.

Evolution Roadmap

1

Phase 1: MVP (0-3 months)

Weeks 1-12
  • Launch with 1-2 customers (100 reports/day)
  • Prove core value (data ingestion → analysis → report)
  • Validate product-market fit
2

Phase 2: Scale (3-6 months)

Weeks 13-24
  • Grow to 10 customers (1,000 reports/day)
  • Add advanced features (custom queries, backtesting)
  • Improve reliability (99.5% SLA)
3

Phase 3: Enterprise (6-12 months)

Weeks 25-52
  • Grow to 100+ customers (10,000+ reports/day)
  • Enterprise features (SSO, RBAC, data residency)
  • Regulatory compliance (SOC 2, ISO 27001)

Complete Systems Architecture

9-layer view: Presentation → Security

Presentation
Web Dashboard
Mobile App
Bloomberg Terminal Plugin
Email Client
API Gateway
Load Balancer (ALB/CloudFlare)
Rate Limiter (Redis)
Auth (OIDC/SAML)
API Versioning
Agent Layer
Planner Agent
Executor Agent
Evaluator Agent
Guardrail Agent
Data Ingestion Agent
Analysis Agent
Report Agent
ML Layer
Feature Store (Feast/Tecton)
Model Registry (MLflow)
Offline Training (Airflow)
Online Inference (FastAPI)
Evaluation Loop
Prompt Store
Integration
Bloomberg API Adapter
Reuters Adapter
Alpha Vantage Adapter
PDF Generator
Email Service (SendGrid)
Data
PostgreSQL (metadata)
TimescaleDB (time-series)
Redis (cache)
S3 (reports, logs)
External
Bloomberg Terminal
Reuters Eikon
Alpha Vantage
OpenAI/Anthropic
AWS Comprehend
Observability
Metrics (Prometheus)
Logs (CloudWatch)
Traces (Jaeger)
Dashboards (Grafana)
Alerts (PagerDuty)
Security
KMS (encryption)
WAF (firewall)
Secrets Manager
Audit Trail (CloudTrail)
PII Redaction

Sequence Diagram - Report Generation Flow

UserAPI GatewayPlanner AgentData Ingestion AgentAnalysis AgentEvaluator AgentReport AgentDistributionPOST /reports/generate {securities: [AAPL, MSFT]}Route request, decompose taskFetch latest data for AAPL, MSFTGET /market-data (AAPL, MSFT)Return prices, volumes, newsStore raw + derived featuresRun technical + sentiment analysisPredict sentiment, risk scoresValidate analysis qualityConfidence: 0.92 (pass)Generate PDF reportRender report with chartsSend via email + Bloomberg200 OK, report_id: abc123

Financial Analysis System - Agent Orchestration

7 Components
[RPC]Analysis request[RPC]Policy check[Event]Fetch market data[gRPC]Analysis job[RPC]Generate report[Event]Data ready[gRPC]Analysis results[Event]Quality metrics[RPC]Pre-publish check[Event]Compliance status[RPC]Validate outputOrchestrator Agent4 capabilitiesExecution Agent4 capabilitiesValidation Agent4 capabilitiesCompliance Agent4 capabilitiesData Ingestion Service4 capabilitiesML Analysis Engine4 capabilitiesReport Generator4 capabilities
HTTP
REST
gRPC
Event
Stream
WebSocket

Financial Analysis System - External Integrations

12 Components
[WebSocket]Real-time quotes[REST]Fundamental data[REST]Technical data[REST]Sentiment data[HTTP]Filing documents[REST]Analysis requests[WebSocket]Live updates[REST]User queries[Push]Alert notifications[Event]Report delivery[S3 API]Report archive[Webhook]Team alerts[REST]Bot commands[Event]Audit logs[REST]Policy updatesCore Trading Intelligence4 capabilitiesBloomberg Terminal3 capabilitiesReuters Refinitiv3 capabilitiesAlpha Vantage3 capabilitiesTwitter/X API3 capabilitiesSEC EDGAR3 capabilitiesTrader Dashboard3 capabilitiesMobile App3 capabilitiesEmail Service3 capabilitiesS3 Storage3 capabilitiesSlack Workspace3 capabilitiesCompliance Portal3 capabilities
HTTP
REST
gRPC
Event
Stream
WebSocket

Data Flow - Request to Report

User request → PDF report in 12 seconds

1
User0 ms
Submits analysis requestSecurities: [AAPL, MSFT], Type: Technical
2
API Gateway50 ms
Authenticates, rate limitsJWT token validated
3
Planner Agent100 ms
Decomposes into subtasksTask DAG: Ingest → Analyze → Report
4
Guardrail Agent200 ms
Policy check (parallel)Compliance: PASS
5
Data Ingestion Agent1500 ms
Fetches market dataBloomberg API: AAPL, MSFT prices, volumes, news
6
Feature Store200 ms
Stores raw + derived featuresRSI, MACD, sentiment scores
7
Analysis Agent2000 ms
Runs ML modelsSentiment: 0.72, Risk: Medium, Signal: BUY
8
Evaluator Agent500 ms
Validates analysisConfidence: 0.89, Quality: PASS
9
Report Agent3000 ms
Generates PDF with chartsPDF (2.3 MB) with 5 charts
10
Distribution1000 ms
Sends via email + BloombergEmail sent, Bloomberg Terminal notified
11
Audit Logger100 ms
Logs all actionsAudit trail stored (7 years retention)
12
UserTotal: ~9 sec
Receives report200 OK, report_id: abc123

Scaling Patterns

Volume
0-100 reports/day
Pattern
Serverless Monolith
Architecture
AWS Lambda (Python)
API Gateway
PostgreSQL (RDS)
S3 (reports)
CloudWatch (logs)
Cost
$200/mo
8-12 sec
Volume
100-1,000 reports/day
Pattern
Queue + Workers
Architecture
API server (FastAPI)
Message queue (SQS/RabbitMQ)
Worker pool (ECS/Fargate)
PostgreSQL + Redis
S3 + CloudFront
Cost
$800/mo
5-8 sec
Volume
1,000-10,000 reports/day
Pattern
Multi-Agent Orchestration
Architecture
Load balancer (ALB)
Agent framework (LangGraph)
Event bus (Kafka/EventBridge)
Serverless functions (Lambda)
Managed DB (Aurora)
Feature Store (Feast)
Cost
$3000/mo
3-5 sec
Volume
10,000-100,000+ reports/day
Pattern
Enterprise Multi-Region
Architecture
Kubernetes (EKS/GKE)
Event streaming (Kafka)
Multi-LLM (GPT-4, Claude, local)
Replicated DB (Aurora Global)
CDN (CloudFront)
Multi-region failover
Cost
$15000+/mo
1-3 sec

Key Integrations

Bloomberg Terminal API

Protocol: Bloomberg API (BLPAPI) over TCP
Connect to Bloomberg server
Subscribe to real-time data feed
Request historical data (BDH)
Normalize to internal schema
Store in Feature Store

Reuters Eikon API

Protocol: REST API (JSON)
Authenticate with client credentials
GET /data/historical (time-series)
GET /data/news (articles)
Parse JSON responses
Deduplicate with Bloomberg data

Alpha Vantage (Backup)

Protocol: REST API (JSON)
GET /query?function=TIME_SERIES_DAILY
Parse JSON, extract OHLCV
Use only if Bloomberg/Reuters unavailable
Lower priority in Feature Store

PDF Generation

Protocol: Local library (WeasyPrint/Puppeteer)
Render HTML template (Jinja2)
Generate charts (Plotly → PNG)
Convert HTML to PDF (WeasyPrint)
Upload to S3
Generate signed URL (expires 7 days)

Email Distribution (SendGrid)

Protocol: REST API (JSON)
POST /v3/mail/send
Attach PDF from S3 URL
Track opens/clicks
Handle bounces/unsubscribes

Security & Compliance

Failure Modes & Fallbacks

FailureFallbackImpactSLA
Bloomberg API downSwitch to Reuters → Alpha Vantage → Cached data (flag staleness)Degraded (stale data), not broken99.5%
LLM API timeout (GPT-4)Retry 3x → Switch to Claude → Rule-based analysisSlower (5-10 sec delay), reduced quality99.0%
ML model prediction low confidence (<0.7)Flag for human review → Use historical average → Skip predictionManual review required (30 min delay)99.9%
PDF generation failsGenerate HTML report → Text-only email → Manual generationFormat degraded, content intact99.5%
Database unavailableRead from replica → Use Redis cache → Fail gracefullyRead-only mode (no new reports)99.9%
Guardrail agent detects policy violationBlock processing → Alert compliance team → Log incidentProcessing halted (safety first)100%
Data ingestion rate limit exceededQueue requests → Throttle to 80% limit → Batch processingDelayed (5-15 min), not lost99.0%

Advanced ML/AI Patterns

Production ML engineering beyond basic API calls

RAG vs Fine-Tuning

Hallucination Detection

Evaluation Framework

Dataset Curation

Agentic RAG

Model Monitoring & Drift Detection

Tech Stack Summary

LLMs
GPT-4 (OpenAI), Claude 3.5 (Anthropic), Gemini (Google)
Orchestration
LangGraph (multi-agent), Airflow (batch jobs), Temporal (workflows)
Database
PostgreSQL (metadata), TimescaleDB (time-series), Redis (cache)
Queue
AWS SQS (simple), Kafka (high-throughput), RabbitMQ (complex routing)
Compute
AWS Lambda (serverless), ECS/Fargate (containers), EKS (Kubernetes)
ML
Feast (feature store), MLflow (model registry), SageMaker (training)
Monitoring
Prometheus (metrics), Grafana (dashboards), Jaeger (traces), PagerDuty (alerts)
Security
AWS KMS (encryption), Secrets Manager (secrets), WAF (firewall), Comprehend (PII)
Data Sources
Bloomberg API, Reuters Eikon, Alpha Vantage, Yahoo Finance
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