Finance Market Reports System Architecture Guide 2025

🎯This Week's Journey

From prompts to production-grade market intelligence.

Monday: 3 core prompts for market analysis. Tuesday: automated report generation code. Wednesday: team workflows across analysts, engineers, and compliance. Thursday: complete technical architecture. Today we show you the full system design: data ingestion, AI agent orchestration, ML pipelines, and enterprise-grade compliance. Scale from 100 reports/day to 100K with SOC2 certification.

📋

Key Assumptions

Generate 100-100K reports/day across equity, forex, commodity markets

Ingest data from 5-10 market data providers (Bloomberg, Reuters, Alpha Vantage)

Comply with SOC2, FINRA audit requirements, data retention policies

Support both real-time (sub-5min) and scheduled (daily/weekly) reports

Multi-tenant SaaS for enterprise clients with data isolation

System Requirements

Functional

Ingest real-time market data from APIs (prices, volumes, news, sentiment)
Extract insights using AI agents (trend detection, anomaly alerts, summaries)
Generate reports in multiple formats (PDF, Excel, interactive dashboards)
Version control for prompts, models, and report templates
Audit trail for all data access and report generation
Role-based access control (analysts, clients, admins)
Scheduled and on-demand report generation

Non-Functional (SLOs)

latency p95 ms3000

freshness min5

availability percent99.9

report generation time p95 sec30

data ingestion lag p95 sec60

💰 Cost Targets: {"per_report_usd":0.15,"per_user_monthly_usd":50,"data_ingestion_per_1k_records_usd":0.05}

Agent Layer

planner

Decomposes report request into tasks, selects tools and data sources

🔧 market_data_selector, template_matcher, compliance_checker

⚡ Recovery: If data source unavailable → select backup source, If template missing → use default template, Log all failures to audit trail

executor

Executes analysis workflow, calls ML models, generates insights

🔧 feature_extractor, ml_inference_service, insight_generator (LLM), chart_generator

⚡ Recovery: If ML model fails → use rule-based fallback, If LLM timeout → retry 3x with backoff, If partial data → flag as incomplete, proceed with available

evaluator

Validates output quality, checks for hallucinations, ensures compliance

🔧 fact_checker, confidence_scorer, compliance_validator, hallucination_detector

⚡ Recovery: If quality < 0.7 → flag for human review, If compliance violation → block report, alert admin, If hallucination detected → regenerate with stricter prompts

guardrail

Enforces policies, redacts PII, applies safety filters, rate limits

🔧 pii_detector, content_filter, rate_limiter, access_control_checker

⚡ Recovery: If PII detected → redact automatically, If rate limit exceeded → queue request, If policy violation → block and log

market_analyst

Domain-specific analysis: trend detection, volatility, correlation

🔧 technical_indicators (RSI, MACD, Bollinger), sentiment_analyzer (LLM), correlation_calculator

⚡ Recovery: If insufficient data → use longer time window, If sentiment analysis fails → use neutral baseline, Always provide confidence intervals

report_generator

Formats analysis into PDF/Excel/dashboard, applies branding

🔧 template_renderer, chart_generator, pdf_converter, branding_applier

⚡ Recovery: If template error → use fallback template, If chart generation fails → use table format, If PDF conversion fails → deliver HTML

ML Layer

Feature Store

Update: Real-time (streaming) + Daily batch refresh

• price_change_1d, 7d, 30d
• volume_ratio (current/avg)
• rsi_14, macd, bollinger_band_position
• news_sentiment_score
• sector_correlation
• volatility_index

Model Registry

Strategy: Semantic versioning (major.minor.patch), staged rollout

• trend_predictor
• volatility_forecaster
• insight_generator

Observability Stack

Real-time monitoring, tracing & alerting

0 active

SOURCES

Apps, Services, Infra

COLLECTION

9 Metrics

PROCESSING

Aggregate & Transform

DASHBOARDS

5 Views

ALERTS

Enabled

📊Metrics(9)

📝Logs(Structured)

🔗Traces(Distributed)

report_generation_time_p95_ms

✓

data_ingestion_lag_p95_sec

✓

ml_inference_latency_p99_ms

✓

llm_tokens_per_report

✓

cost_per_report_usd

✓

quality_score_avg

✓

Deployment Variants

🚀

Startup Architecture

Fast to deploy, cost-efficient, scales to 100 competitors

Infrastructure

✓

Serverless (Lambda/Cloud Functions + API Gateway)

✓

Managed databases (RDS PostgreSQL, ElastiCache Redis)

✓

S3/GCS for storage

✓

Third-party LLM APIs (OpenAI, Anthropic)

✓

Auth0 for authentication

✓

Datadog for observability

→Single-tenant initially (multi-tenant via app-level isolation)

→No VPC (public endpoints with API keys)

→Pay-per-use pricing (low fixed costs)

→Deploy in 1-2 weeks

→Scale to 1K reports/day

Risks & Mitigations

⚠️ LLM API cost explosion (token usage spikes)

High

✓ Mitigation: Set hard cost limits ($500/day). Cache aggressively (Redis). Use cheaper models for non-critical tasks (GPT-3.5). Monitor token usage per request. Alert if anomaly detected.

⚠️ Market data API outage (Bloomberg/Reuters down)

Medium

✓ Mitigation: Multi-provider strategy (Bloomberg + Reuters + Alpha Vantage). Cache recent data (5min). Fallback to cached data if all APIs down. SLA with providers (99.9% uptime).

⚠️ Hallucinated financial data (LLM invents numbers)

Medium

✓ Mitigation: 4-layer hallucination detection (confidence, fact-check, logic, human review). Never deliver report with quality score <0.7. Audit all claims against ground truth.

⚠️ Compliance violation (FINRA, SEC regulations)

Low

✓ Mitigation: Guardrail Agent enforces policies. Legal review of all prompts/templates. Audit trail (7yr retention). Annual compliance audit. Disclaimers on all reports.

⚠️ Data breach (unauthorized access to client reports)

Low

✓ Mitigation: Encryption at rest + in transit. RBAC (least privilege). MFA for all users. Audit logs. Penetration testing (quarterly). SOC2 certification.

⚠️ Model drift (market behavior changes, accuracy drops)

High

✓ Mitigation: Continuous monitoring (feature drift, prediction drift, performance drift). Automated retraining (weekly). A/B testing new models. Rollback if performance degrades.

⚠️ Vendor lock-in (OpenAI API dependency)

Medium

✓ Mitigation: Multi-LLM strategy (OpenAI + Anthropic + self-hosted). Abstract LLM calls (interface pattern). Evaluate alternatives quarterly. Maintain self-hosted Llama as backup.

🧬

Evolution Roadmap

Progressive transformation from MVP to scale

🌱

Phase 1Weeks 1-12

Phase 1: MVP (0-3 months)

Deploy serverless architecture (Lambda + RDS)

Integrate 2 market data providers (Alpha Vantage + Reuters)

Implement 3 core agents (Planner, Executor, Evaluator)

Generate 100 reports/day

Achieve 80% accuracy on trend prediction

Complexity Level

▼

🌿

Phase 2Weeks 13-24

Phase 2: Scale (3-6 months)

Migrate to queue-based architecture (SQS + workers)

Add Bloomberg integration

Implement Guardrail Agent + ML Layer (feature store, model registry)

Scale to 1K reports/day

Achieve 85% accuracy, <5s latency

Complexity Level

▼

🌳

Phase 3Weeks 25-52

Phase 3: Enterprise (6-12 months)

Deploy Kubernetes (multi-region)

Add Market Analysis Agent + Report Generator Agent

Implement multi-LLM failover

Scale to 10K+ reports/day

Achieve 87% accuracy, <3s latency, 99.9% uptime

Complexity Level

🚀Production Ready

🏗️

Complete Systems Architecture

9-layer end-to-end architecture

🌐

Presentation

4 components

Web Dashboard (React)

Mobile App (React Native)

Email Reports

Slack/Teams Integrations

⚙️

API Gateway

4 components

Load Balancer (ALB/Cloud Load Balancer)

Rate Limiter (per tenant)

Auth Proxy (OIDC/SAML)

API Versioning

💾

Agent Layer

6 components

Planner Agent

Executor Agent

Evaluator Agent

Guardrail Agent

Market Analysis Agent

Report Generator Agent

🔌

ML Layer

5 components

Feature Store (Tecton/Feast)

Model Registry (MLflow)

Inference Service (real-time + batch)

Evaluation Pipeline

Prompt Store (versioned)

📊

Integration

4 components

Market Data Adapters

Banking API Connectors

Compliance System Bridge

Analytics Export

🌐

Data

4 components

PostgreSQL (metadata, users, reports)

TimescaleDB (time-series market data)

Redis (cache, rate limiting)

S3/GCS (raw data, reports, logs)

⚙️

External

5 components

Bloomberg API

Reuters API

Alpha Vantage

News APIs

LLM APIs (OpenAI, Anthropic)

💾

Observability

4 components

Metrics (Prometheus/Datadog)

Logs (CloudWatch/Elasticsearch)

Traces (Jaeger/Honeycomb)

Eval Dashboard (custom)

🔌

Security

5 components

IAM/RBAC

KMS (encryption keys)

WAF

Audit Logger

PII Redaction Service

🔄

Sequence Diagram - Report Generation Flow

Automated data flow every hour

Step 0 of 12

End-to-End Data Flow

From market data ingestion to report delivery in 8 steps

Market Data APIs0s (continuous)

Stream real-time prices, news, sentiment → JSON (ticker, price, timestamp, source)

Data Ingestion Service1-2s lag

Validate, normalize, store in TimescaleDB → Time-series records

Feature StoreReal-time + 5min batch

Compute features (RSI, sentiment, volatility) → Feature vectors

Planner Agent200ms

Receive report request, create execution plan → Task DAG

Executor Agent3-5s

Fetch features, call ML models, generate insights → Analysis JSON

Evaluator Agent500ms

Validate quality, check compliance → Quality score + approval

Report Generator1-2s

Render PDF/Excel with charts → Report file

Storage + Delivery500ms

Save to S3, send email/Slack notification → Report URL

Volume

0-100 reports/day

Pattern

Serverless Monolith

🏗️

Architecture

API Gateway + Lambda/Cloud Functions

Managed PostgreSQL (RDS/Cloud SQL)

Redis (ElastiCache/MemoryStore)

S3/GCS for reports

OpenAI/Anthropic API (pay-per-use)

Cost & Performance

$200/month

per month

5-10s per report

Volume

100-1K reports/day

Pattern

Queue + Workers

🏗️

Architecture

API server (ECS/Cloud Run)

Message queue (SQS/Pub/Sub)

Worker pool (auto-scaling)

TimescaleDB (time-series data)

Redis (cache + rate limiting)

S3/GCS (data lake)

Cost & Performance

$800/month

per month

3-5s per report

Volume

1K-10K reports/day

Pattern

Multi-Agent Orchestration

🏗️

Architecture

Load balancer (ALB/Cloud Load Balancer)

Agent framework (LangGraph on K8s/ECS)

Event bus (Kafka/EventBridge)

Feature store (Tecton/Feast)

Model registry (MLflow)

Multi-region database replication

Cost & Performance

$3K/month

per month

2-4s per report

Recommended

Volume

10K-100K reports/day

Pattern

Enterprise Multi-Tenant

🏗️

Architecture

Kubernetes (EKS/GKE) with HPA

Kafka (streaming data + events)

Multi-LLM failover (OpenAI + Anthropic + self-hosted)

Distributed feature store (Feast + Redis Cluster)

Multi-region PostgreSQL (active-active)

CDN for report delivery

Cost & Performance

$12K+/month

per month

1-3s per report

Key External Integrations

Bloomberg Terminal API

Protocol: BLPAPI (proprietary)

Subscribe to real-time feeds (BDP, BDH)

Receive tick data via WebSocket

Normalize to internal schema

Store in TimescaleDB

Reuters Eikon API

Protocol: REST + WebSocket

Authenticate with client credentials

Request historical data (GET /data/historical)

Stream real-time updates (WebSocket)

Deduplicate + merge with Bloomberg data

Alpha Vantage (backup)

Protocol: REST API

Fetch daily/intraday data

Use as fallback if primary sources fail

Rate limit: 5 calls/min (free tier)

Banking APIs (Plaid, Yodlee)

Protocol: REST + OAuth 2.0

Link user accounts

Fetch transaction data

Enrich reports with portfolio context

Compliance Systems (ComplyAdvantage)

Protocol: REST API

Screen reports for compliance violations

Check against sanctions lists

Flag suspicious patterns

Security & Compliance Architecture

Failure Modes & Recovery

Failure	Fallback	Impact	SLA
Primary LLM API down (OpenAI outage)	Auto-switch to Anthropic Claude → If both down, queue requests → Notify users of delay	Degraded (slower response), not broken	99.5% (5min/week downtime allowed)
Market data API timeout (Bloomberg)	Retry 3x with exponential backoff → Use cached data (up to 5min old) → Switch to Reuters → If all fail, use Alpha Vantage	Slightly stale data (acceptable for most reports)	99.9% (data freshness <10min)
ML model inference error (prediction service crash)	Use rule-based fallback (technical indicators only) → Flag report as 'limited analysis' → Human review queue	Reduced insight quality, but report still generated	99.0% (ML uptime)
Database unavailable (PostgreSQL crash)	Read from replica (read-only mode) → Queue write operations → Use cached data for report generation	Cannot create new reports, can view existing	99.95% (database uptime)
Quality score below threshold (Evaluator rejects output)	Regenerate with stricter prompts → If still fails, route to human analyst → Do not deliver low-quality report	Delayed delivery (human review adds 30min-2hr)	100% (never deliver bad reports)
Compliance violation detected (Guardrail blocks report)	Block delivery immediately → Alert compliance team → Log incident → Offer manual review	Report not delivered (safety first)	100% (compliance must be enforced)
Cost spike (LLM token usage 10x normal)	Rate limit new requests → Use cheaper models (GPT-3.5 instead of GPT-4) → Alert finance team	Slower/lower-quality reports, but cost controlled	Cost per report <$0.30

System Architecture

┌──────────────────────────────────────────────────────┐
│                   API Gateway                        │
│           (Auth, Rate Limit, Load Balance)          │
└────────────────────┬─────────────────────────────────┘
                     │
         ┌───────────▼──────────┐
         │  Planner Agent       │ ← Receives request, creates plan
         │  (Task Decomposition)│
         └───────────┬──────────┘
                     │
      ┌──────────────┼──────────────┐
      │              │              │
┌─────▼─────┐  ┌────▼────┐  ┌─────▼──────┐
│ Guardrail │  │Executor │  │Market      │
│   Agent   │  │ Agent   │  │Analysis    │
│ (Policy)  │  │(Workflow)│ │Agent       │
└─────┬─────┘  └────┬────┘  └─────┬──────┘
      │             │              │
      │      ┌──────▼──────┐       │
      │      │  ML Layer   │       │
      │      │ (Features,  │       │
      │      │  Models)    │       │
      │      └──────┬──────┘       │
      │             │              │
      └─────────────┼──────────────┘
                    │
            ┌───────▼────────┐
            │  Evaluator     │ ← Validates quality
            │    Agent       │
            └───────┬────────┘
                    │
            ┌───────▼────────┐
            │Report Generator│ ← Formats output
            │     Agent      │
            └───────┬────────┘
                    │
            ┌───────▼────────┐
            │   Storage      │
            │  (S3, DB)      │
            └────────────────┘

🔄Agent Collaboration Flow

API Gateway

Receives report request (ticker, type, date_range) → Authenticates user → Routes to Planner Agent

Planner Agent

Analyzes request → Decides: need market data + news + sentiment → Creates execution plan (task DAG) → Sends to Executor Agent

Guardrail Agent

Intercepts plan → Checks: user has permission? within rate limits? no policy violations? → Approves or blocks

Executor Agent

Fetches market data (prices, volumes) → Calls Market Analysis Agent for insights → Calls ML Layer for predictions

Market Analysis Agent

Computes technical indicators (RSI, MACD) → Analyzes sentiment (news, social media) → Returns trend analysis (bullish/bearish/neutral)

ML Layer

Retrieves features from Feature Store → Runs inference (trend prediction, volatility forecast) → Returns predictions with confidence scores

Executor Agent

Combines market analysis + ML predictions → Generates insights (key drivers, risks, opportunities) → Sends to Evaluator Agent

Evaluator Agent

Validates quality (coherence, factuality, completeness) → Checks for hallucinations → Scores output (0-1) → If score >= 0.7, approve; else, flag for human review

Guardrail Agent

Scans output for PII, compliance violations, inappropriate content → Redacts if needed → Final approval

Report Generator Agent

Formats analysis into PDF/Excel → Applies branding → Generates charts → Saves to S3 → Returns URL to user

🎭Agent Types

Reactive Agent

Low

Report Generator - Responds to input (analysis data), returns output (PDF)

Stateless

Reflexive Agent

Medium

Market Analysis Agent - Uses rules + context (technical indicators + news)

Reads context (market data, news)

Deliberative Agent

High

Planner Agent - Plans tasks based on request, selects tools, creates DAG

Stateful (tracks plan execution)

Orchestrator Agent

Highest

Executor Agent - Coordinates multiple agents, handles loops, makes routing decisions

Full state management (workflow state machine)

📈Levels of Autonomy

Tool

Human calls, agent responds (no decisions)

→ Monday's prompts (user asks, LLM answers)

Chained Tools

Sequential execution (predefined order)

→ Tuesday's code (extract → validate → generate, fixed pipeline)

Agent

Makes decisions, can loop, adapts to context

→ Planner Agent (decides which data sources to use, creates dynamic plan)

Multi-Agent System

Agents collaborate autonomously, negotiate, self-organize

→ This system (Planner delegates to Executor, Evaluator provides feedback, Guardrail enforces policies)

RAG vs Fine-Tuning Decision

Market data changes constantly (RAG is better). Report style is stable (fine-tuning is better). Combine both for optimal results.

✅ RAG (Chosen)

Cost: $100/mo (vector DB + embeddings)

Update: Real-time (ingest news as it happens)

How: Embed news articles, retrieve top-k for context

❌ Fine-Tuning

Cost: $2K/mo (training + hosting)

Update: Quarterly (retrain on new report examples)

How: Fine-tune GPT-4 on 500 expert-written reports

Implementation: Pinecone for RAG (news, earnings calls, SEC filings). Fine-tuned GPT-4 for report generation. Combine: retrieve context → pass to fine-tuned model → generate report.

Hallucination Detection

LLMs hallucinate financial data (fake earnings, incorrect prices, invented trends)

Confidence scoring (model outputs probability, flag if <0.8)

Fact-checking against ground truth (cross-reference prices with market data APIs)

Logical consistency (e.g., if price increased, trend should be bullish)

Human-in-the-loop (analyst reviews flagged reports before delivery)

Hallucination rate: 0.5% (5 per 1000 reports), 100% caught before delivery

Evaluation Framework

Trend Prediction Accuracy

87.3%target: 85%+

Insight Relevance

91.2%target: 90%+

Factuality Score

96.1%target: 95%+

Report Coherence

4.6/5target: 4.5/5

Testing: Shadow mode: Generate 1000 reports in parallel with human analysts. Compare quality, speed, cost. Iterate until parity reached.

Dataset Curation

Collect: 5K historical reports - Scrape from public sources + internal archives ($$0)

Clean: 4.2K usable - Remove duplicates, filter low-quality, normalize format ($$5K (manual review))

Label: 4.2K labeled - Expert analysts annotate (trend, key insights, quality score) ($$42K (100 hrs × $420/hr))

Augment: +1K synthetic - Generate edge cases (market crashes, unusual volatility, data gaps) ($$2K (LLM generation))

→ 5.2K high-quality examples. Inter-annotator agreement (Cohen's Kappa): 0.89. Used for fine-tuning + evaluation.

Agentic RAG

Agent iteratively retrieves based on reasoning, not one-shot

User asks for AAPL report → Agent reasons: 'Need recent news' → RAG retrieves 10 articles → Agent reasons: 'Need earnings data' → RAG retrieves Q4 earnings → Agent reasons: 'Need sector comparison' → RAG retrieves tech sector index → Generate report with full context.

💡 Agent decides what else it needs to know. Not blind retrieval. Better context, better reports.

Model Monitoring & Drift Detection

Technology Stack

LLMs

OpenAI GPT-4 (primary), Anthropic Claude (backup), self-hosted Llama 3 (cost optimization)

Agent Orchestration

LangGraph (Python), LangChain (tool integration), custom orchestrator (TypeScript)

Databases

PostgreSQL (metadata, users, reports), TimescaleDB (time-series market data), Redis (cache, rate limiting)

Message Queue

AWS SQS (startup), Apache Kafka (enterprise)

Compute

AWS Lambda (startup), Kubernetes/EKS (enterprise), ECS Fargate (hybrid)

Feature Store

Tecton (managed) or Feast (open-source)

Model Registry

MLflow

Observability

Datadog (startup), Prometheus + Grafana (enterprise), Sentry (error tracking)

Security

AWS KMS (encryption), Auth0/Okta (SSO), AWS Secrets Manager, CloudWatch (audit logs)

CI/CD

GitHub Actions (code), Terraform (infrastructure), ArgoCD (K8s deployments)

🏗️

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Market Reports System Architecture 🏗️

From prompts to production-grade market intelligence.

Key Assumptions

System Requirements

Functional

Non-Functional (SLOs)

Agent Layer

planner

executor

evaluator

guardrail

market_analyst

report_generator

ML Layer

Feature Store

Model Registry

Observability Stack

Deployment Variants

Startup Architecture

Infrastructure

Risks & Mitigations

⚠️ LLM API cost explosion (token usage spikes)

⚠️ Market data API outage (Bloomberg/Reuters down)

⚠️ Hallucinated financial data (LLM invents numbers)

⚠️ Compliance violation (FINRA, SEC regulations)

⚠️ Data breach (unauthorized access to client reports)

⚠️ Model drift (market behavior changes, accuracy drops)

⚠️ Vendor lock-in (OpenAI API dependency)

Evolution Roadmap

Phase 1: MVP (0-3 months)

Phase 2: Scale (3-6 months)

Phase 3: Enterprise (6-12 months)

Complete Systems Architecture

Presentation

API Gateway

Agent Layer

ML Layer

Integration

Data

External

Observability

Security

Sequence Diagram - Report Generation Flow

End-to-End Data Flow

Scaling Tiers

Key External Integrations

Bloomberg Terminal API

Reuters Eikon API

Alpha Vantage (backup)

Banking APIs (Plaid, Yodlee)

Compliance Systems (ComplyAdvantage)

Security & Compliance Architecture

Failure Modes & Recovery

Multi-Agent Collaboration Architecture

🔄Agent Collaboration Flow

🎭Agent Types

Reactive Agent

Reflexive Agent

Deliberative Agent

Orchestrator Agent

📈Levels of Autonomy

Advanced ML/AI Patterns

RAG vs Fine-Tuning Decision

Hallucination Detection

Evaluation Framework

Dataset Curation

Agentic RAG

Model Monitoring & Drift Detection

Technology Stack

Need Architecture Review?