FIML

Financial Intelligence Meta-Layer

The Future of Financial MCP: A Meta-Layer That Sits Above All Data Providers

An AI-native financial data MCP server with intelligent provider orchestration, delivering unified access to market intelligence through a powerful Financial Knowledge DSL and multi-agent analysis framework.

Explore the Vision Read the 10-Year Plan See Current Progress

What is FIML?

FIML is the intelligent meta-layer that transforms how AI agents access financial data. Instead of managing multiple providers, FIML orchestrates them automatically—selecting the best sources, handling fallbacks, and delivering unified, reliable market intelligence.

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Unified Financial Access

A single MCP interface for stocks, crypto, forex, and macro indicators. One API for all your financial data needs.

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Intelligent Provider Arbitration

Automatic provider scoring, fallback handling, conflict resolution, and multi-source data fusion—ensuring you always get the best answer.

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Agent-Ready FK-DSL

Financial Knowledge DSL and multi-agent orchestration enable complex queries like "EVALUATE TSLA: PRICE, VOLATILITY(30d), CORRELATE(BTC, SPY)".

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Long-Term Ecosystem Vision

A 10-year roadmap toward a Financial OS—plugin ecosystem, decentralized verification, global coverage, and AI-native portfolio optimization.

Concept & Vision

The Meta-Layer Paradigm

FIML is designed as the world's first AI-native financial operating system: a meta-provider abstraction layer that orchestrates data from dozens of sources, runs multi-agent analysis, and serves AI agents with stateful, context-aware insights.

Rather than forcing developers to integrate with individual data providers—each with different APIs, rate limits, reliability, and coverage—FIML presents a unified Model Context Protocol (MCP) interface that intelligently routes queries to the best available sources.

Financial MCP: A Control Plane for Intelligence

The MCP server acts as a control plane for three critical functions:

Data Access: Real-time and historical market data across equities, crypto, forex, and macro indicators.
Analysis: Multi-agent orchestration for fundamentals, technicals, sentiment, correlation, and risk analysis.
Event Intelligence: Real-time market events, watchdog triggers, and narrative generation for market-moving developments.

Today vs FIML Future

Today (Multiple Providers)

FIML Meta-Layer

Manage 5+ separate API keys and SDKs

One unified MCP interface

Manual fallback and error handling per provider

Automatic provider scoring and fallback

Conflicting data from different sources

Intelligent conflict resolution and data fusion

Custom query logic for each analysis type

Financial Knowledge DSL for complex queries

No stateful multi-step analysis

Session-based multi-agent orchestration

Core Value Proposition

Every AI agent gets the best possible financial answer through intelligent data arbitration, multi-source fusion, and context-aware analysis—without managing individual data providers.

10-Year Plan: 2025–2035

FIML's journey from foundation to global Financial OS—a decade-long vision to build the world's most intelligent financial data layer.

Phase 1: Foundation

Q1 2025 — COMPLETE

Technical: Core MCP server with FastAPI, data arbitration engine, provider abstraction layer
Technical: FK-DSL parser with complete Lark-based grammar
Technical: Multi-agent orchestration structure using Ray
Technical: L1 (Redis) and L2 (PostgreSQL/TimescaleDB) cache architecture
Data/Providers: Yahoo Finance provider implementation, mock provider for testing
Infrastructure: Docker, Kubernetes configs, GitHub Actions CI/CD, comprehensive test suite

Phase 2: Intelligence Layer

Q2–Q3 2025

Technical: Real-time WebSocket/SSE streaming for live market data
Technical: Advanced multi-agent workflows for complex analysis
Technical: Narrative generation engine for AI-powered market summaries
Technical: Compliance and safety framework with regional routing
Data/Providers: Alpha Vantage, FMP, CCXT crypto exchange integrations
Infrastructure: Cache warming, enhanced error handling and retry logic

Phase 3: Scale & Platform

Q4 2025 – 2028

Technical: Multi-language support and localization framework
Technical: Advanced analytics and machine learning integration
Technical: Performance optimization for enterprise-scale deployments
Platform: Official integrations with ChatGPT, Claude Desktop, Telegram bots
Data/Providers: Extended market coverage across global exchanges
Ecosystem: Developer documentation, SDK libraries, community tooling

Phase 4+: Ecosystem & Financial OS

2028 – 2035

Technical: Plugin ecosystem where third-party providers and strategies integrate seamlessly
Technical: Decentralized data verification layer for trust and transparency
Technical: Advanced quantitative strategies and ML-driven analytics
Technical: AI-native portfolio optimization and risk management dashboards
Global Expansion: Multi-market localization, regional compliance, worldwide coverage
Ecosystem: Full Financial OS layer serving as infrastructure for next-gen fintech

Current Progress

Phase 1 Complete Version 0.1.0

Foundation: Core Infrastructure Complete

All Phase 1 goals have been achieved. FIML now has a working MCP server with intelligent data arbitration, provider abstraction, cache layers, FK-DSL parser, multi-agent framework, and production-ready infrastructure.

Now (Phase 1 — Delivered)

✅ Data Arbitration Engine with provider scoring and fallback
✅ Provider Abstraction with Yahoo Finance and Mock implementations
✅ L1 (Redis) and L2 (PostgreSQL/TimescaleDB) cache layers
✅ Complete FK-DSL parser with Lark grammar
✅ Ray-based multi-agent orchestration structure
✅ FastAPI MCP server with core tools (search-by-symbol, search-by-coin, execute-fk-dsl)
✅ Docker and Kubernetes configurations
✅ CI/CD with GitHub Actions
✅ Comprehensive unit and integration test suite

Next 6–12 Months (Phase 2)

🔄 Additional provider integrations: Alpha Vantage, FMP, CCXT exchanges
🔄 Real-time streaming via WebSocket and Server-Sent Events
🔄 Advanced multi-agent workflows for complex analysis
🔄 Compliance framework with regional routing and safety checks
🔄 Narrative generation engine for AI-powered market summaries
🔄 Cache optimization with pre-warming and intelligent eviction
🔄 Enhanced error handling and retry logic across all layers

Architecture

FIML's layered architecture orchestrates data from multiple providers, runs intelligent analysis, and delivers unified results through a single MCP interface.

Client Layer

AI agents, applications, and tools that consume FIML. Includes ChatGPT plugins, Claude Desktop integrations, Telegram bots, custom applications, and developer tools.

Unified MCP API Gateway

Request routing, authentication, rate limiting, and compliance enforcement. Presents a consistent Model Context Protocol interface regardless of underlying complexity.

Core Intelligence Layer

FK-DSL parser for complex query interpretation, session store for stateful multi-step analysis, compliance router for regional requirements, and narrative generation engine for market summaries.

Data Arbitration Engine

The heart of FIML's intelligence: real-time provider scoring, automatic fallback execution, multi-source data merging, and conflict resolution.

Provider Scoring: Evaluates freshness, latency, uptime, completeness, and historical reliability
Arbitration Planning: Builds execution plans with primary provider, fallback chain, merge strategy, and expected latency
Auto-Fallback Execution: Seamlessly switches providers on failure without client intervention
Data Fusion: Intelligently merges OHLCV, fundamentals, and sentiment from multiple sources
Conflict Resolution: Outlier detection, trust scoring, historical accuracy weighting, and recency bias

Multi-Agent Orchestration

Ray-based distributed agent framework for parallel analysis across multiple dimensions:

Fundamental Agent: Earnings, revenue, margins, growth metrics
Technical Agent: Price action, RSI, MACD, volume patterns
Macro Agent: Interest rates, inflation, currency movements
Sentiment Agent: Social media, news sentiment, market positioning
Correlation Agent: Cross-asset relationships and factor analysis
Risk/Anomaly Agent: Unusual patterns, risk metrics, anomaly detection
News Agent: Event extraction, narrative tracking, impact assessment

Ultra-Fast Cache Layer

Multi-tier caching strategy for optimal performance:

L1 In-Memory (Redis): 10–100ms latency for hot data and real-time queries
L2 Persistent (PostgreSQL/TimescaleDB): 300–700ms for historical time-series data
Predictive Pre-Warming: Anticipates queries and pre-fetches popular data

Data Provider Abstraction

Pluggable provider architecture supporting diverse data sources:

Current: Yahoo Finance (implemented), Mock provider (testing)
Planned: Alpha Vantage, FMP, CCXT exchanges, Token Metrics, custom scrapers, public APIs
Future: Third-party plugin ecosystem for community-contributed providers

Unified Event Stream

Real-time event delivery via WebSocket hub, Server-Sent Events, and webhooks. Watchdog events for price alerts, volume spikes, and market-moving developments.

Query Flow: AI Agent → FIML → Providers → Result

Agent Request: AI agent sends query via MCP (e.g., "Get TSLA price and volatility")
Gateway Routing: API gateway authenticates, rate-limits, and routes to intelligence layer
FK-DSL Parsing: Query interpreted into structured execution plan
Arbitration Planning: Engine scores providers and builds fallback strategy
Cache Check: L1/L2 caches consulted for existing data
Provider Execution: If cache miss, primary provider queried (auto-fallback on failure)
Agent Orchestration: Multi-agent analysis runs in parallel (technical, fundamental, etc.)
Data Fusion & Resolution: Multi-source data merged, conflicts resolved
Cache Population: Result stored in L1 and L2 for future queries
Response: Unified, validated result returned to AI agent

Get Involved

FIML is an open journey toward a Financial OS. Join us in building the future of AI-native financial intelligence.

⭐ Watch the Repository

Follow development progress, releases, and updates on GitHub.

Visit GitHub

🚀 Run FIML Locally

Try FIML on your machine with Docker:

git clone https://github.com/kiarashplusplus/FIML.git
cd FIML
docker-compose up

See the README for detailed setup instructions and configuration options.

🤝 Contribute

Help build the Financial OS:

Implement new data providers
Add tests and documentation
Create FK-DSL query examples
Build new analysis agents
Develop platform integrations

🌍 Join the Journey

FIML is a 10-year vision to transform financial data infrastructure. Whether you're a developer, institution, or enthusiast—your participation shapes the future of AI-native finance.

Latest Updates

Announcing FIML: A 10-Year Financial Intelligence Meta-Layer Blueprint

January 2025

Today we're unveiling FIML—the Financial Intelligence Meta-Layer—and our ambitious 10-year roadmap to build the world's first AI-native financial operating system.

The financial data landscape is fragmented. Developers integrate with multiple providers, each with different APIs, reliability levels, and coverage. AI agents struggle to get consistent, high-quality market data. This complexity creates friction and limits what's possible.

FIML changes this paradigm. Instead of managing providers individually, FIML presents a unified Model Context Protocol interface that intelligently orchestrates data access. Our Data Arbitration Engine scores providers in real-time, handles fallbacks automatically, merges multi-source data, and resolves conflicts—delivering the best possible answer to every query.

We've completed Phase 1 (Foundation) with a working MCP server, arbitration engine, provider abstraction, FK-DSL parser, multi-agent framework, and production infrastructure. Over the next 10 years, FIML will evolve from this foundation into a full Financial OS—complete with plugin ecosystem, decentralized verification, global coverage, and AI-native analytics.

This is a long-term journey, and we're building in the open. Join us on GitHub, contribute providers and agents, and help shape the future of financial intelligence.

Welcome to the meta-layer.