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)
💰 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
L4Decompose 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
L3Execute 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
L3Validate 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
L4Policy 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
L2Fetch 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
L3Run 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
L2Generate 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
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
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)
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
Sequence Diagram - Report Generation Flow
Financial Analysis System - Agent Orchestration
7 ComponentsFinancial Analysis System - External Integrations
12 ComponentsData Flow - Request to Report
User request → PDF report in 12 seconds
Scaling Patterns
Key Integrations
Bloomberg Terminal API
Reuters Eikon API
Alpha Vantage (Backup)
PDF Generation
Email Distribution (SendGrid)
Security & Compliance
Failure Modes & Fallbacks
Failure | Fallback | Impact | SLA |
---|---|---|---|
Bloomberg API down | Switch to Reuters → Alpha Vantage → Cached data (flag staleness) | Degraded (stale data), not broken | 99.5% |
LLM API timeout (GPT-4) | Retry 3x → Switch to Claude → Rule-based analysis | Slower (5-10 sec delay), reduced quality | 99.0% |
ML model prediction low confidence (<0.7) | Flag for human review → Use historical average → Skip prediction | Manual review required (30 min delay) | 99.9% |
PDF generation fails | Generate HTML report → Text-only email → Manual generation | Format degraded, content intact | 99.5% |
Database unavailable | Read from replica → Use Redis cache → Fail gracefully | Read-only mode (no new reports) | 99.9% |
Guardrail agent detects policy violation | Block processing → Alert compliance team → Log incident | Processing halted (safety first) | 100% |
Data ingestion rate limit exceeded | Queue requests → Throttle to 80% limit → Batch processing | Delayed (5-15 min), not lost | 99.0% |
Advanced ML/AI Patterns
Production ML engineering beyond basic API calls