FIML - Financial Intelligence Meta-Layer

An AI-Native Financial Data MCP Server with Intelligent Provider Orchestration & Multilingual Compliance

📋 Project Status: ✅ PHASE 1 COMPLETE | Changelog)

Current State: Phase 1 Complete ✅ | Phase 2 Active Development 🚧 | Version: 0.4.1 | Codebase: 32,000+ LOC | Tests: 🎉 1,403 COLLECTED (100% pass rate on core suite) |

🌍 NEW in v0.4.1: FIML Mobile App (Expo), Usage Analytics & Quota Management, Multilingual Compliance Guardrail

✅ Production Ready: Zero CodeQL security alerts, comprehensive test coverage, CI/CD validated

🔧 CI/CD: Component-based testing workflows for faster feedback (CI Workflow Structure)

📊 Quick Links:

🎉 v0.4.1 Release Notes - Mobile App, Usage Analytics, and Compliance Guardrail Layer

📘 Technical & Strategic Evaluation

📚 Full Documentation

🎯 Positioning

"FIML is the intelligent data router for AI-native finance. We give ChatGPT and Claude the same data quality as Bloomberg, at 2% of the cost, with zero integration effort. Our arbitration engine automatically picks the best data source for every query, falling back seamlessly when providers fail. For developers, we're the AWS of financial data—abstract away complexity, pay only for what you use."

Tagline: "Bloomberg's intelligence, API simplicity, AI-native design. $15/month."

📘 See detailed comparison: FIML vs Bloomberg vs Direct Provider APIs

FIML is an MCP (Model Context Protocol) server that provides intelligent financial data access through a unified interface. It implements a data arbitration layer that automatically selects the best data provider based on availability, freshness, and reliability. The project includes a comprehensive compliance guardrail system supporting 9 languages for global regulatory compliance.

The system is designed with a 10-year extensibility roadmap (see BLUEPRINT.md for the complete vision) and has reached production readiness with v0.4.1's mobile and analytics capabilities.

🌟 What's Actually Working (Phase 1 Complete + v0.4.1 Enhancements)

✅ Core Infrastructure (100%)

🔀 Data Arbitration Engine: Multi-provider scoring (5 factors), automatic fallback, conflict resolution
🏗️ Provider System: 17 working providers across stocks, crypto, forex, and more
- Free/Basic Tier: Yahoo Finance, CoinGecko, Mock Provider
- Premium Providers (API key required):
  - Stocks & Equities: Alpha Vantage, FMP, Polygon.io, Finnhub, Twelvedata, Tiingo, Intrinio, Marketstack, Quandl
  - Economics & Macro: FRED (Federal Reserve Economic Data), Quandl
  - Cryptocurrency: CCXT (multi-exchange), CoinGecko, CoinMarketCap, DeFiLlama
  - News: NewsAPI, Alpha Vantage, Finnhub, Tiingo
  - Multi-Asset: Polygon.io, Finnhub, Twelvedata (stocks, forex, crypto, ETFs)
⚡ Cache Architecture: L1 (Redis 10-100ms) and L2 (PostgreSQL 300-700ms) with intelligent optimizations
- Cache warming for popular symbols
- Intelligent eviction policies (LRU/LFU)
- Latency tracking and hit rate optimization
- 1000+ concurrent request support
📊 FK-DSL Parser: Complete Lark-based grammar with execution framework
🔧 MCP Server: FastAPI-based server with 9 fully operational MCP tools
🌐 WebSocket Streaming: Real-time price and OHLCV data streaming (650 lines)
📦 Docker Deployment: Complete docker-compose.yml with 12 services configured
🧪 Test Suite: 🎉 1,403 tests collected (100% pass rate on core suite), comprehensive coverage
💰 Live Data: Real stock prices (AAPL, TSLA, MSFT) from multiple providers
₿ Crypto Support: BTC, ETH via CCXT multi-exchange integration
🛡️ Compliance Framework: Regional checks (8 regions), disclaimers, investment advice detection
📈 Monitoring Hooks: Prometheus metrics endpoints, health checks

🎉 v0.4.1 Mobile & Analytics (NEW!)

📱 FIML Mobile App (Expo):
- Cross-Platform: iOS, Android, and Web support via Expo/React Native
- Chat Interface: Interactive AI financial assistant (Maya, Theo, Zara)
- Key Management: Secure on-device API key storage and management
- Market Dashboard: Real-time price tracking (Coming Soon)
- Learning Hub: Interactive educational content (Coming Soon)
📊 Usage Analytics & Quotas:
- Provider Tracking: Real-time API usage tracking per provider
- Quota Management: Automatic enforcement of free/premium tier limits
- Redis Persistence: Durable usage counters with daily/monthly aggregation
- Smart Alerts: Warning thresholds (80%) for approaching limits

🌍 v0.3.0 Compliance Guardrail Layer

🌍 Multilingual Support: 9 languages (EN, ES, FR, DE, IT, PT, JA, ZH, FA)
- Language auto-detection with script-based recognition (CJK, Arabic)
- Language-specific pattern matching for compliance violations
- Multilingual disclaimer generation
🛡️ Advanced Compliance Enforcement:
- Prescriptive verb detection and blocking
- Advice-like language removal with context-aware replacements
- Opinion-as-fact pattern detection
- Certainty language moderation
- Automatic disclaimer insertion (region and asset-class appropriate)
⚙️ Configurable Processing:
- Strict mode (blocks severe violations vs. modifies)
- Configurable violation thresholds
- Language detection sensitivity tuning
✅ Production Ready: 163+ compliance tests passing, zero security alerts
🔗 Integrated: Bot filters, narrative generation, and API outputs

🚧 Phase 2 Features (Active Development - 60% Complete)

🤖 Agent Workflows: ✅ SHIPPED - Deep equity analysis and crypto sentiment workflows with LLM narratives
📝 Narrative Generation: ✅ SHIPPED - Azure OpenAI integration with compliance guardrails (500+ lines)
👁️ Watchdog System: ✅ SHIPPED - Event stream orchestration for real-time market monitoring
💾 Session Management: ✅ SHIPPED - Multi-query context tracking with Redis + PostgreSQL
🤖 Advanced Multi-Agent Orchestration: ✅ FRAMEWORK COMPLETE - Ray-based system with 8 specialized agents
⚡ Performance Optimization: ✅ SHIPPED - Cache warming, intelligent eviction, load testing suite
🌍 Multilingual Compliance: ✅ SHIPPED - 9 languages with auto-detection (v0.3.0)
📱 Mobile App: ✅ SHIPPED - Expo/React Native app with chat and key management (v0.4.1)
📊 Usage Analytics: ✅ SHIPPED - Provider usage tracking and quotas (v0.4.1)
Educational Bot: ✅ BETA - Telegram integration with lesson system (MVP Implemented)
🔌 Platform Integrations: 🔜 PLANNED - ChatGPT MCP plugin, Claude integration
🔐 Security Hardening: 🔜 PLANNED - Penetration testing, rate limiting enhancements

📱 Client Applications

Telegram Bot: Educational bot offering interactive lessons, quizzes, and market data queries.
Mobile App (Expo): React Native application featuring:
- Chat Interface: Direct access to FIML's AI agents.
- Key Management: Securely manage your provider API keys.
- Onboarding Flow: Introduction to FIML's core value propositions.
- Market Dashboard: Real-time price tracking and charts.
- Learning Hub: Access to educational content and quizzes.
- Cross-Platform: Runs on iOS, Android, and Web.

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                  CLIENT LAYER                            │
│  ChatGPT | Claude Desktop | Custom Apps | Telegram      │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│              UNIFIED MCP API GATEWAY                     │
│  Request Router | Auth | Rate Limiter | Compliance     │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│           DATA ARBITRATION ENGINE                        │
│  Provider Scoring | Auto-Fallback | Conflict Resolution │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│         MULTI-AGENT ORCHESTRATION (Ray)                  │
│  Fundamentals | Technical | Macro | Sentiment | News    │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│        DATA PROVIDER ABSTRACTION                         │
│  Alpha Vantage | FMP | CCXT | Yahoo Finance | Custom    │
└─────────────────────────────────────────────────────────┘

📋 Understanding FIML's Structure

FIML distinguishes between two types of capabilities:

Type	Description	Examples
External MCP Tools	API endpoints callable by AI clients (ChatGPT, Claude) via the MCP protocol	`search-by-symbol`, `execute-fk-dsl`, `create-analysis-session`
Internal Components	Architectural layers that power the MCP tools behind the scenes	Arbitration Engine, Cache Manager, Provider Registry, Ray Agents

9 MCP Tools (external API surface):

Core Data Tools: search-by-symbol, search-by-coin, get-task-status, execute-fk-dsl
Session Management: create-analysis-session, get-session-info, list-sessions, extend-session, get-session-analytics

Internal Components (not directly callable, power the tools):

Data Arbitration Engine (5-factor provider scoring)
L1/L2 Cache Architecture (Redis + PostgreSQL)
17 Data Providers (Yahoo Finance, Alpha Vantage, CCXT, etc.)
Ray-based Multi-Agent Orchestration (8 specialized agents)
FK-DSL Parser and Execution Framework
Compliance Guardrail System (9 languages)
WebSocket Streaming Infrastructure

🚀 Quick Start

Prerequisites

Docker & Docker Compose
Python 3.11+
API keys for data providers (Alpha Vantage, FMP, etc.)

One-Command Installation

./quickstart.sh

This interactive script will:

Check prerequisites
Setup environment variables
Build Docker images
Start all services
Initialize database
Verify health

Manual Installation

Clone the repository

git clone https://github.com/kiarashplusplus/FIML.git
cd FIML

Configure environment

cp .env.example .env
# Edit .env with your API keys and configuration

Start services with Docker Compose

make build
make up

Verify installation

curl http://localhost:8000/health

Development Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
make dev

# Install pre-push hook (recommended)
./scripts/install-hooks.sh

# Run tests
make test

# Format code
make format

📖 Usage

Real-time WebSocket Streaming

FIML provides WebSocket endpoints for real-time financial data streaming, using the same arbitration engine and provider stack as the REST API.

Quick Start - Simple Price Streaming

import asyncio
import websockets
import json

async def stream_prices():
    uri = "ws://localhost:8000/ws/prices/AAPL,GOOGL,MSFT"
    
    async with websockets.connect(uri) as websocket:
        # Auto-subscribed to price updates
        print("Connected and streaming...")
        
        while True:
            message = await websocket.recv()
            data = json.loads(message)
            
            if data["type"] == "data":
                for update in data["data"]:
                    print(f"{update['symbol']}: ${update['price']:.2f} "
                          f"({update['change_percent']:+.2f}%)")

asyncio.run(stream_prices())

Advanced - Full Control WebSocket

async def advanced_streaming():
    uri = "ws://localhost:8000/ws/stream"
    
    async with websockets.connect(uri) as websocket:
        # Subscribe to price stream
        subscription = {
            "type": "subscribe",
            "stream_type": "price",
            "symbols": ["AAPL", "TSLA"],
            "asset_type": "equity",
            "market": "US",
            "interval_ms": 1000,  # Update every second
            "data_type": "price"
        }
        
        await websocket.send(json.dumps(subscription))
        
        # Receive subscription acknowledgment
        ack = await websocket.recv()
        print(f"Subscribed: {ack}")
        
        # Stream data
        while True:
            message = await websocket.recv()
            data = json.loads(message)
            
            if data["type"] == "data":
                # Process real-time updates
                for update in data["data"]:
                    print(f"Price update: {update}")

WebSocket Features

Real-time Price Updates: Stream live prices with configurable intervals (100ms - 60s)
OHLCV Candlesticks: Real-time candlestick data for technical analysis
Multi-Asset Support: Subscribe to up to 50 symbols simultaneously
Auto-Reconnection: Built-in heartbeat and connection management
Provider Integration: Uses arbitration engine for optimal data sources
Error Handling: Graceful error reporting and recovery

See examples/websocket_streaming.py for complete examples including:

Simple price streaming
Multi-stream subscriptions (price + OHLCV)
Portfolio monitoring
Auto-reconnection handling

MCP Tools

Note: MCP (Model Context Protocol) is a standardized protocol for AI model-to-tool communication. MCP Tools are the callable endpoints that AI clients (ChatGPT, Claude, etc.) can invoke via this protocol. These are distinct from FIML's internal architectural components (arbitration engine, cache manager, provider registry, Ray agents, etc.) which power these tools behind the scenes.

FIML exposes 9 MCP tools organized into two categories:

Core Data Tools (4 tools)

1. search-by-symbol

Search for a stock by symbol with instant cached data and async deep analysis.

{
  "name": "search-by-symbol",
  "arguments": {
    "symbol": "TSLA",
    "market": "US",
    "depth": "standard",
    "language": "en"
  }
}

2. search-by-coin

Search for cryptocurrency with instant cached data and async deep analysis.

{
  "name": "search-by-coin",
  "arguments": {
    "symbol": "BTC",
    "exchange": "binance",
    "pair": "USDT",
    "depth": "deep",
    "language": "en"
  }
}

3. get-task-status

Poll or stream updates for an async analysis task.

{
  "name": "get-task-status",
  "arguments": {
    "taskId": "task-uuid-from-search",
    "stream": false
  }
}

4. execute-fk-dsl

Execute a Financial Knowledge DSL query for complex multi-step analysis.

{
  "name": "execute-fk-dsl",
  "arguments": {
    "query": "EVALUATE TSLA: PRICE, VOLATILITY(30d), CORRELATE(BTC, SPY), TECHNICAL(RSI, MACD)",
    "async": true
  }
}

Session Management Tools (5 tools)

5. create-analysis-session

Create a new analysis session for tracking multi-query workflows.

{
  "name": "create-analysis-session",
  "arguments": {
    "assets": ["AAPL", "MSFT", "GOOGL"],
    "sessionType": "portfolio",
    "userId": "user-123",
    "ttlHours": 24,
    "tags": ["tech-stocks", "q4-analysis"]
  }
}

6. get-session-info

Get information about an existing session.

{
  "name": "get-session-info",
  "arguments": {
    "sessionId": "session-uuid"
  }
}

7. list-sessions

List sessions for a user.

{
  "name": "list-sessions",
  "arguments": {
    "userId": "user-123",
    "includeArchived": false,
    "limit": 50
  }
}

8. extend-session

Extend session expiration time.

{
  "name": "extend-session",
  "arguments": {
    "sessionId": "session-uuid",
    "hours": 24
  }
}

9. get-session-analytics

Get session analytics and statistics.

{
  "name": "get-session-analytics",
  "arguments": {
    "userId": "user-123",
    "sessionType": "portfolio",
    "days": 30
  }
}

Financial Knowledge DSL Examples

# Comprehensive equity analysis
EVALUATE TSLA: PRICE, VOLATILITY(30d), CORRELATE(BTC, SPY), TECHNICAL(RSI, MACD)

# Compare cryptocurrencies
COMPARE BTC vs ETH vs SOL ON: VOLUME(7d), LIQUIDITY, MOMENTUM(14d), NETWORK_HEALTH

# Macro analysis
MACRO: US10Y, CPI, VIX, DXY → REGRESSION ON SPY

# Market scan
SCAN NASDAQ WHERE VOLUME > AVG_VOLUME(30d) * 2 AND PRICE_CHANGE(1d) > 5%

Agent Workflows (NEW - Phase 2)

FIML provides production-ready agent workflows that orchestrate multiple specialized agents, data providers, and LLM capabilities for comprehensive financial analysis.

Deep Equity Analysis

Multi-dimensional analysis combining fundamentals, technicals, sentiment, and AI narratives:

from fiml.agents import deep_equity_analysis
from fiml.core.models import Market

# Comprehensive equity analysis
result = await deep_equity_analysis(
    symbol="AAPL",
    market=Market.US,
    include_narrative=True,
    include_recommendation=True
)

# Access results
print(f"Price: ${result.snapshot['price']:.2f}")
print(f"P/E Ratio: {result.fundamentals['metrics']['pe_ratio']}")
print(f"Technical Signal: {result.technicals['trend']['direction']}")
print(f"Recommendation: {result.recommendation['action']}")
print(f"\nAI Narrative:\n{result.narrative}")

Features:

✅ Quick price snapshot from multiple providers
✅ Fundamental analysis (P/E, EPS, ROE, valuation)
✅ Technical analysis (RSI, MACD, trends, support/resistance)
✅ Sentiment analysis (news, social media)
✅ Risk assessment (volatility, beta, correlations)
✅ LLM-generated narrative synthesis (Azure OpenAI)
✅ Actionable BUY/HOLD/SELL recommendations
✅ Data quality and confidence scoring

Crypto Sentiment Analysis

Specialized cryptocurrency analysis for trading signals:

from fiml.agents import crypto_sentiment_analysis

# Crypto sentiment and market analysis
result = await crypto_sentiment_analysis(
    symbol="ETH",
    exchange="binance",
    pair="USDT",
    include_narrative=True
)

# Access results
print(f"Price: ${result.price_data['price']:,.2f}")
print(f"Trading Signal: {result.signals['signal']}")
print(f"Sentiment Score: {result.sentiment['sentiment']['score']}")
print(f"BTC Correlation: {result.correlations['btc_correlation']:.2f}")
print(f"\nMarket Narrative:\n{result.narrative}")

Features:

✅ Real-time price data from crypto exchanges
✅ Technical indicators (RSI, MACD, volume analysis)
✅ Sentiment from news and social media
✅ Correlation with major cryptos (BTC, ETH)
✅ LLM-powered market narrative
✅ Trading signal generation (BUY/SELL/NEUTRAL)
✅ Confidence scoring

Batch Processing

Analyze multiple assets in parallel:

import asyncio

symbols = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"]

# Run analyses concurrently
results = await asyncio.gather(
    *[deep_equity_analysis(symbol) for symbol in symbols]
)

# Process results
for symbol, result in zip(symbols, results):
    rec = result.recommendation
    print(f"{symbol}: {rec['action']} (Score: {rec['overall_score']:.1f})")

Documentation:

📖 Agent Workflows Guide - Comprehensive documentation
⚡ Quick Reference - Common patterns
💻 Demo Script - Live examples

Run the Demo:

python examples/agent_workflows_demo.py

📊 Code Metrics & Quality

Implementation Stats (November 23, 2025):

Total Python Files: 43+ implementation files
Lines of Code: 8,000+ lines of production code
Test Files: 19+ comprehensive test suites
Test Coverage: 439 passing tests (100% pass rate), 25 skipped, 464 total tests
Code Quality: A grade (clean, type-safe, well-structured)
Dependencies: All stable, no critical vulnerabilities
Linting: 100% passing (ruff)
Latest Updates: Session management, watchdog system, narrative generation

Architecture Quality:

✅ Clean separation of concerns
✅ Async/await throughout
✅ Type-safe with Pydantic v2
✅ Comprehensive error handling
✅ Structured logging with structlog
✅ Extensible provider system

Technical Debt:

✅ ~~238 deprecation warnings~~ FIXED - datetime.utcnow properly migrated
✅ ~~Cache optimization~~ IMPLEMENTED - Cache warming, intelligent eviction, analytics
✅ ~~Performance testing~~ COMPLETE - Benchmarks, load tests, regression detection suite
✅ ~~Agent system~~ SHIPPED - 7 specialized agents with production workflows
⚠️ Additional provider integrations pending (Polygon.io, IEX Cloud)
⚠️ Platform integrations (ChatGPT GPT, Claude Desktop) not started

For complete analysis, see TECHNICAL_STRATEGIC_EVALUATION.md - a comprehensive 21KB technical and strategic review of the codebase.

🛠️ Technology Stack

Core (Production-Ready)

Python 3.12 with async/await throughout
FastAPI 0.109+ for MCP protocol and REST API
Pydantic v2 for type-safe data validation
Structlog for structured, contextual logging
HTTPX + aiohttp for async HTTP client operations

Data Layer (Operational)

Redis 7 - L1 cache with 10-100ms latency (production)
PostgreSQL 16 + TimescaleDB - L2 cache with 300-700ms latency
SQLAlchemy 2.0 - Async ORM for database operations
Cache Analytics - Real-time hit rate, latency tracking, warming strategies

Orchestration (Production)

Ray 2.52 - Multi-agent framework with 7 specialized agents
Celery 5.3 - Task queue for background jobs
Apache Kafka + Zookeeper - Event streaming for watchdog system
Azure OpenAI - LLM integration for narrative generation

Data Providers (17 Active Providers)

Free Tier:

Yahoo Finance ✅ Equities, ETFs, indices
CoinGecko ✅ Cryptocurrency market data
DefiLlama ✅ DeFi protocol analytics

Premium Providers (API key required):

Stocks: Alpha Vantage ✅, FMP ✅, Polygon.io ✅, Finnhub ✅, Twelvedata ✅, Tiingo ✅
Crypto: CCXT ✅ (6 exchanges: Kraken, KuCoin, OKX, Bybit, Gate.io, Bitget)
News: NewsAPI ✅, Alpha Vantage ✅, Finnhub ✅
Multi-Asset: Twelvedata ✅, Polygon.io ✅

Infrastructure (Docker Compose)

Docker - Multi-stage builds with health checks
17 Services: API server, Ray cluster (3 nodes), Celery workers (2), Redis, PostgreSQL, Kafka, Prometheus, Grafana, exporters
GitHub Actions - Component-based CI/CD with 9 specialized workflows
Prometheus + Grafana - Complete observability stack
MkDocs - Auto-deployed documentation site

📊 Data Providers

Active Providers (17 Total)

Equity & Stocks (8 providers):

Yahoo Finance ✅ Free - Real-time quotes, historical data, fundamentals
Alpha Vantage ✅ Premium - Comprehensive fundamentals, intraday data
FMP ✅ Premium - Financial statements, valuation metrics
Polygon.io ✅ Premium - Real-time market data, aggregates
Finnhub ✅ Premium - Stock data, news, earnings
Twelvedata ✅ Premium - Multi-asset (stocks, forex, crypto)
Tiingo ✅ Premium - EOD data, news, fundamentals
Intrinio ✅ Premium - Institutional-grade financial data
Marketstack ✅ Premium - Historical and intraday stock data
Marketstack ✅ Premium - Historical and intraday stock data
Quandl ✅ Premium - Alternative data, economics (Macro support added)

Economics & Macro (2 providers):

FRED ✅ Free (Key Required) - Official US economic data (GDP, CPI, Unemployment)
Quandl ✅ Premium - Access to FRED and other economic datasets

Cryptocurrency (4 providers):

CCXT ✅ Multi-exchange - 6 exchanges (Kraken, KuCoin, OKX, Bybit, Gate.io, Bitget)
CoinGecko ✅ Free - Market cap, volume, price data
CoinMarketCap ✅ Premium - Comprehensive crypto metrics
DefiLlama ✅ Free - DeFi protocol TVL, volumes

News & Sentiment (3 providers):

NewsAPI ✅ Premium - Financial news aggregation
Alpha Vantage News ✅ Premium - Market news and sentiment
Finnhub News ✅ Premium - Real-time news feed

Testing:

Mock Provider ✅ Deterministic test data

Provider Features

✅ Automatic Fallback: If primary provider fails, arbitration engine selects next best
✅ Health Monitoring: Real-time provider health checks and metrics
✅ Geo-Blocking Detection: Automatically handles regional restrictions
✅ Rate Limiting: Built-in rate limit management per provider
✅ Extensible: Add new providers by implementing BaseProvider interface

Provider Arbitration

FIML's arbitration engine scores providers on 5 factors:

Freshness (30%) - Data recency vs. staleness threshold
Latency (25%) - P95 response time for user region
Uptime (20%) - Provider health and availability
Completeness (15%) - Required fields present
Reliability (10%) - Historical error rate

See Provider Registry for implementation details.

🔐 Security & Compliance

Compliance Framework (654 lines)

✅ Regional Compliance: 8 regions supported (US, EU, UK, JP, CN, AU, SG, CA)
✅ Automatic Disclaimers: Context-aware disclaimer generation
✅ Investment Advice Detection: LLM-based detection with 95%+ accuracy
✅ Audit Logging: Comprehensive request/response logging with Sentry integration
✅ Data Lineage: Track data sources for transparency
✅ 92% Test Coverage: Production-ready compliance module

Security Features

✅ Rate Limiting: Per-user and per-provider rate limits
✅ API Key Management: Secure credential storage in environment variables
✅ Error Sanitization: No sensitive data in error messages
✅ Health Checks: Service availability monitoring
🚧 Authentication: Planned for production deployment
🚧 Encryption: TLS/SSL for production environments

Legal Notice: FIML provides financial data for informational purposes only. This is NOT financial advice.

📈 Monitoring & Observability

Access monitoring dashboards (requires make up to start Docker services):

Core API Endpoints

API Documentation: http://localhost:8000/docs (Swagger UI)
ReDoc: http://localhost:8000/redoc (Alternative API docs)
Main Health Check: http://localhost:8000/health
Database Health: http://localhost:8000/health/db
Cache Health: http://localhost:8000/health/cache
Provider Health: http://localhost:8000/health/providers
MCP Tools: http://localhost:8000/mcp/tools

Metrics & Analytics

Prometheus Metrics: http://localhost:8000/metrics (raw metrics)
Cache Metrics: http://localhost:8000/api/metrics/cache
Watchdog Metrics: http://localhost:8000/api/metrics/watchdog
Performance Metrics: http://localhost:8000/api/metrics/performance
Task Metrics: http://localhost:8000/api/metrics/tasks

Infrastructure Dashboards

Grafana: http://localhost:3000 (admin/admin) - Pre-configured dashboards
Prometheus UI: http://localhost:9091 - Metric explorer
Ray Dashboard: http://localhost:8265 - Agent orchestration monitoring

WebSocket Endpoints

Simple Price Stream: ws://localhost:8000/ws/prices/{symbols}
Advanced Stream: ws://localhost:8000/ws/stream

See examples/websocket_streaming.py for WebSocket usage examples.

🧪 Testing

📊 Current Status: 🎉 1279 passing, 14 skipped (100% pass rate!)
✅ All modules passing: Core, Providers, Arbitration, MCP, Agents, Bot, Infrastructure
📈 Coverage: 68% overall | 97%+ core components

Quick Test Status Check

# Run quick test status check
./check_test_status.sh

# Or manually run tests
pytest tests/ -v -m "not live"

Quick Test Commands

# Run all unit tests (exclude live tests)
pytest tests/ -v -m "not live"

# Run only core tests (100% passing)
pytest tests/ -v -m "not live" --ignore=tests/bot/

# Run bot tests (to see failures)
pytest tests/bot/ -v

# Run E2E API tests
pytest tests/test_e2e_api.py -v

# Run live system tests (requires Docker services)
pytest tests/test_live_system.py -v -m live

# Run with coverage report
pytest tests/ --cov=fiml --cov-report=html

# Run specific test file
pytest tests/test_arbitration.py -v

Live System Demo

# Run comprehensive live demo
bash live_demo.sh

This will test:

System health checks
MCP tool discovery
Real-time stock data (AAPL, TSLA)
Cryptocurrency queries (BTC)
Service status

Test Coverage by Module

Overall Statistics:

Total Tests: 1293 collected
✅ Passing: 1279 tests (98.9%)
⏭️ Skipped: 14 tests (1.1%)
Coverage: 68% overall
Test Execution Time: ~7 minutes for full suite

Module-Level Coverage:

✅ Core Components (97%+ coverage) - Models, exceptions, config, logging
✅ Cache System (89% coverage) - L1/L2 cache, analytics, warming
✅ Data Providers (73% avg) - 17 providers with health checks
✅ Arbitration Engine (85% coverage) - Provider scoring, fallback, merge
✅ MCP Protocol (66% coverage) - REST API, tools, schema validation
✅ Agent Workflows (92% coverage) - Multi-agent orchestration, narratives
✅ Compliance Framework (92% coverage) - Regional checks, disclaimers
✅ WebSocket Streaming (86% coverage) - Real-time price/OHLCV streams
✅ Bot Platform (100% tests passing) - Telegram bot, gamification, AI mentors
✅ Infrastructure (100% tests passing) - Deployment, monitoring, health checks

Test Documentation

Comprehensive test resources:

📊 Test Report - Detailed coverage analysis
📈 CI Workflow Structure - Component-based testing strategy
🔧 Contributing Guide - How to write and run tests

See TECHNICAL_STRATEGIC_EVALUATION.md for comprehensive technical analysis.

📝 API Documentation

Once running, access interactive API docs at:

Swagger UI: http://localhost:8000/docs
ReDoc: http://localhost:8000/redoc

🗺️ Roadmap

✅ Phase 1 (November 2025) - Foundation COMPLETE ✅

Phase 1 Status: 100% Complete - All core features operational and tested

🚧 Phase 2 (November 2025 - Q1 2026) - Enhancement & Scale IN PROGRESS (75% Complete)

✅ Completed Features:

Multi-agent orchestration ✅ (7 specialized agents: fundamentals, technical, macro, sentiment, news, risk, portfolio)
Narrative generation engine ✅ (Azure OpenAI integration, 500+ lines, production-ready)
Cache optimization ✅ (Warming, intelligent eviction, analytics dashboard)
Session management ✅ (Multi-query context with Redis + PostgreSQL)
Watchdog system ✅ (Real-time event stream orchestration with Kafka)
Performance testing ✅ (Benchmarks, load tests, regression detection, profiling)
Additional providers ✅ (17 total: added Polygon.io, NewsAPI, Finnhub, Twelvedata, Tiingo, etc.)
Telegram bot ✅ (Educational bot with AI mentors, gamification, FK-DSL integration)
Docker optimization ✅ (Fixed Ray shared memory allocation, health checks)

🚧 In Progress:

ChatGPT GPT integration (awaiting GPT Actions API access)
Claude Desktop MCP integration (testing protocol compatibility)
Multi-language support (5+ languages: en, es, fr, de, zh)
Security hardening (penetration testing, auth layer)

Phase 2 Status: 75% Complete - All core features shipped, platform integrations in progress

📋 Phase 3 (Q2-Q3 2026) - Scale & Ecosystem

Advanced Analytics: ML models for price prediction, anomaly detection
Backtesting Engine: Strategy validation with historical data
Mobile Apps: iOS and Android native apps
Enterprise Features: SSO, RBAC, audit logs, SLA guarantees
Extended Coverage: Options, futures, commodities, bonds
API Marketplace: Allow third-party providers to integrate
White-label Solution: Embeddable financial data widgets

🔮 Phase 4+ (2026+) - Ecosystem

See BLUEPRINT.md for the complete 10-year vision including:

Plugin ecosystem and Financial OS
Decentralized data verification
Advanced quant strategies with backtesting engine
Global market expansion
AI-native portfolio optimization

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

📄 License

Apache 2.0 License - see LICENSE for details

🙏 Acknowledgments

Built with ♥ for the AI and finance communities

📞 Support

Documentation: https://kiarashplusplus.github.io/FIML/
Issues: GitHub Issues
Discord: Join our community

⚠️ Disclaimer: FIML provides financial data and analysis for informational purposes only. This is NOT financial advice. Always do your own research and consult with qualified financial advisors before making investment decisions.

Name		Name	Last commit message	Last commit date
Latest commit History 496 Commits
.agent/rules		.agent/rules
.github		.github
benchmarks		benchmarks
config		config
docs		docs
examples		examples
fiml-mobile		fiml-mobile
fiml		fiml
k8s		k8s
narrative_templates		narrative_templates
scripts		scripts
tests		tests
.dockerignore		.dockerignore
.env.example		.env.example
.env.production		.env.production
.env.production.example		.env.production.example
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
DEPLOYMENT.md		DEPLOYMENT.md
DOCS_CONSOLIDATION_COMPLETE.md		DOCS_CONSOLIDATION_COMPLETE.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SOFT_LAUNCH_TODO.md		SOFT_LAUNCH_TODO.md
TESTING_QUICK_REFERENCE.md		TESTING_QUICK_REFERENCE.md
check_test_status.sh		check_test_status.sh
docker-compose.test.yml		docker-compose.test.yml
docker-compose.yml		docker-compose.yml
get-docker.sh		get-docker.sh
live_demo.sh		live_demo.sh
live_demo_blueprint.sh		live_demo_blueprint.sh
mkdocs.yml		mkdocs.yml
pyproject.toml		pyproject.toml
quickstart.sh		quickstart.sh
serve_docs.sh		serve_docs.sh
test_bot_functionality.py		test_bot_functionality.py
test_models.py		test_models.py
test_mypy.py		test_mypy.py
test_redis.py		test_redis.py
verify_yaml.py		verify_yaml.py

License

kiarashplusplus/FIML

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