Open Paper Trading MCP 📈

A comprehensive paper trading simulator with dual interfaces: REST API (FastAPI) and AI agent tools (MCP). Designed for algorithmic trading development, strategy backtesting, options trading simulation, and training AI agents in realistic market environments without financial risk.

🎯 Core Capabilities

• Multi-Asset Trading: Stocks, options, ETFs, and bonds with specialized implementations
• Advanced Options Trading: Full options chain support with Greeks calculations and 15+ spread strategies
• Professional Spread Builder: Iron Condors, Butterflies, Straddles, Strangles, and advanced multi-leg strategies
• Real-time Risk Analysis: P&L diagrams, breakeven calculations, and win probability analysis
• AI Agent Training: Native MCP interface for training trading agents and LLMs
• Production-Ready: Type-safe, async architecture with comprehensive testing and monitoring
• Dual Interface Access: Both REST API (web clients) and MCP tools (AI agents) access identical functionality

✅ Current Status (2025-01-08)

🎉 PRODUCTION READY QUALITY - Successfully implemented and deployed dual-server architecture:

• FastAPI Server (port 2080): Frontend integration + 49 REST API endpoints operational
• MCP Server (port 2081): Independent MCP server with 43 tools + auto-generated list_tools function
• Test Success Rate: 99.8% (576/581 tests passing, comprehensive journey-based testing)
• Code Quality: 100% ruff compliance, 100% mypy clean, all style violations resolved
• Database Integration: PostgreSQL async operations with proper session management
• Service Layer: TradingService fully integrated via dependency injection
• API Documentation: Auto-generated docs available at /docs
• Multi-Account Support: Complete backend implementation with account_id parameter support
• Options Trading: Complete options chain integration with live market data and professional interface
• ADK Evaluation Complete: 42/42 evaluations tested - 100% agent behavior validation with proper multi-step workflows
• MCP Tool Reliability: All 43 tools validated through live agent interaction using real market data
• AsyncIO Infrastructure: Complete resolution of event loop conflicts, 100% test stability

✅ Prerequisites

Before you begin, ensure you have the following installed:

• Docker and Docker Compose: For running the application in a containerized environment.
• Python: Version 3.11 or higher.
• uv: The project's package manager.

🚀 Quick Start

# 1. Clone the repository
git clone https://github.com/yourusername/open-paper-trading-mcp.git
cd open-paper-trading-mcp

# 2. Start everything with Docker
docker-compose up --build

# 3. Services are now available at:
#    - Frontend & API: http://localhost:2080/
#    - MCP Server: http://localhost:2081/
#    - API Docs: http://localhost:2080/docs

🏗️ Architecture Overview

REST Client          AI Agent
     |                  |
     v                  v
FastAPI Server    MCP Server
(Port 2080)      (Port 2081)
     |                  |
     +------------------+
              |
              v
       TradingService
              |
    +---------+---------+
    |                   |
    v                   v
PostgreSQL DB    Robinhood API
(Trading State)  (Market Data)

Split Architecture Benefits:

• Independent Servers: FastAPI (2080) and MCP (2081) run separately, eliminating mounting conflicts
• Dual Interface Access: Web clients use REST API, AI agents use MCP tools - same underlying functionality
• Service Layer Unity: Both interfaces use identical TradingService for consistency
• Database-First: All trading state persisted in PostgreSQL with async operations
• Real-time Market Data: Direct API calls to Robinhood for current market information
• Type Safety: Full Pydantic validation on all inputs/outputs across both interfaces

🏆 Key Achievements & Lessons Learned

Major Technical Achievements

1. AsyncIO Infrastructure Mastery: Resolved 164 AsyncIO event loop conflicts that were causing 49% test failure rate
2. Split Architecture Success: Overcame FastMCP mounting conflicts by implementing independent server architecture
3. Database Session Consistency: Established unified get_async_session() pattern across entire codebase
4. Test Infrastructure Stability: Achieved 99.8% test success rate (576/581 tests passing)
5. Dual Interface Implementation: Successfully created mirror functionality between REST API and MCP tools
6. MCP Tool Validation Complete: 42/42 ADK evaluations completed with 100% agent behavior validation
7. Live Market Data Integration: All tools successfully use real Robinhood API with proper error handling
8. Production Deployment Ready: Docker containers optimized, real data policy enforced, comprehensive monitoring

Critical Lessons Learned

1. Event Loop Management: Create fresh database engines per test in current event loop to prevent AsyncIO conflicts
2. Service Architecture: Independent servers solve mounting conflicts better than complex integration
3. Database Patterns: Always use get_async_session() dependency injection, never AsyncSessionLocal() directly
4. Testing Patterns: Standardized mocking with side_effect for async generators ensures reliable tests
5. Code Quality: Comprehensive linting (ruff), type checking (mypy), and formatting standards prevent technical debt

Development Workflow Optimizations

• Split Development: FastAPI server (frontend/API) and MCP server (AI tools) can be developed independently
• Service Layer Unity: Changes to TradingService automatically benefit both interfaces
• Test-Driven Stability: Comprehensive test coverage (70%+) with AsyncIO-safe patterns
• Live API Integration: Robinhood API tests with @pytest.mark.robinhood for real-world validation

🛠️ Technology Stack

• Backend: Python, FastAPI, FastMCP
• Database: PostgreSQL
• ORM: SQLAlchemy
• Market Data: Robinhood API
• Package Management: uv
• Containerization: Docker, Docker Compose

⚙️ Development

Local Setup (without Docker)

# Install uv package manager
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip sync pyproject.toml

# Set up PostgreSQL and update .env
cp .env.example .env
# Edit DATABASE_URL in .env

# Run the application
uv run python app/main.py

Configuration

The application is configured using environment variables. Copy the .env.example file to .env and update the following variables:

• DATABASE_URL: The connection string for your PostgreSQL database.
• ROBINHOOD_USERNAME: Your Robinhood username (for live market data).
• ROBINHOOD_PASSWORD: Your Robinhood password.
• QUOTE_ADAPTER_TYPE: The quote adapter to use (test or robinhood).

Development Commands

# Format code
python scripts/dev.py format

# Run linting
python scripts/dev.py lint

# Type checking
python scripts/dev.py typecheck

# Run all tests
python scripts/dev.py test

# Run all checks
python scripts/dev.py check

Database Development Patterns

Always use consistent database session patterns:

# ✅ CORRECT - Use get_async_session()
from app.storage.database import get_async_session

async def database_operation():
    async for db in get_async_session():
        result = await db.execute(select(Model))
        return result.scalars().all()

# ❌ INCORRECT - Never use AsyncSessionLocal() directly
from app.storage.database import AsyncSessionLocal
async with AsyncSessionLocal() as db:  # Breaks testing!
    pass

Testing database code:

from unittest.mock import patch

@patch('app.storage.database.get_async_session')
async def test_function(mock_get_session, test_session):
    async def mock_generator():
        yield test_session
    mock_get_session.return_value = mock_generator()
    
    # Your test code here
    result = await database_operation()
    assert result is not None

This ensures consistent behavior between production and testing environments.

🧪 Testing Best Practices

AsyncIO Event Loop Management

Critical for async test stability:

# tests/conftest.py - Create fresh engines per test
@pytest_asyncio.fixture(scope="function")
async def db_session() -> AsyncGenerator[AsyncSession, None]:
    # Create engine in current event loop (critical for AsyncIO compatibility)
    test_engine = create_async_engine(
        database_url, 
        echo=False, 
        future=True,
        pool_pre_ping=True,  # Verify connections before use
        pool_recycle=300     # Recycle connections every 5 minutes
    )
    
    test_session_factory = async_sessionmaker(
        bind=test_engine, 
        class_=AsyncSession, 
        expire_on_commit=False
    )
    
    try:
        async with test_session_factory() as session:
            yield session
    finally:
        await test_engine.dispose()  # Critical for preventing leaks

Common Test Issues & Solutions

1. Missing Await Keywords

# ❌ WRONG - Async method without await
result = adapter.get_account_ids()  # Returns coroutine!
assert len(result) == 3  # TypeError: object of type 'coroutine' has no len()

# ✅ CORRECT - Always await async methods
result = await adapter.get_account_ids()
assert len(result) == 3

2. DateTime Timezone Issues

# ❌ WRONG - Mixed timezone awareness
from datetime import datetime
created_at = datetime.now(timezone.utc)  # timezone-aware
updated_at = datetime.now()              # timezone-naive

# ✅ CORRECT - Consistent timezone handling
from datetime import datetime, timezone
created_at = datetime.now(timezone.utc)
updated_at = datetime.now(timezone.utc)

3. Database Session Mocking Pattern

# ✅ CORRECT - Proper async session mocking
async def test_database_operation(self, db_session: AsyncSession):
    adapter = DatabaseAccountAdapter()
    with patch('app.adapters.accounts.get_async_session') as mock_get_session:
        async def mock_session_generator():
            yield db_session
        mock_get_session.side_effect = lambda: mock_session_generator()
        
        # Test database operations with real session
        result = await adapter.get_account("test-id")
        assert result is not None

Test Infrastructure Achievements

• AsyncIO Event Loop Issues: ✅ RESOLVED - 164 AsyncIO errors eliminated
• Success Rate Improvement: 29% → 99.8% (576/581 tests passing)
• Database Session Consistency: ✅ Implemented across all core functions
• Test Pattern Standardization: ✅ Unified mocking patterns established
• Live API Testing: ✅ Robinhood tests integrated with @pytest.mark.robinhood marker
• Journey-Based Testing: ✅ User journey organization prevents timeout issues with 581 total tests

Live API Testing with Robinhood

The test suite includes integration tests that make live, read-only calls to the Robinhood API:

# Run all tests including live Robinhood calls
uv run pytest

# Exclude live Robinhood API tests (faster, no external dependencies)
uv run pytest -m "not robinhood"

# Run only Robinhood integration tests
uv run pytest -m "robinhood"

Robinhood Test Features:

• Read-only operations: Stock quotes, market data, company information, search
• Rate limiting protection: All marked as @pytest.mark.slow
• Real data validation: Verifies actual API responses and data formats
• Shared fixtures: Consistent setup via trading_service_robinhood fixture