From 959f74ca4072742cefc4977ee256953b8957303b Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Thu, 24 Jul 2025 16:32:51 -0400 Subject: [PATCH 01/11] claude does something crazy --- README.md | 98 +++++- pyproject.toml | 5 + quantconnect-mcp.dxt | Bin 130596 -> 130843 bytes quantconnect_mcp/main.py | 275 +++++++++++++---- quantconnect_mcp/src/server.py | 76 +---- quantconnect_mcp/src/tools/__init__.py | 12 +- quantconnect_mcp/src/tools/data_tools.py | 167 +++++++--- quantconnect_mcp/src/tools/quantbook_tools.py | 284 ++++++++++++++---- quantconnect_mcp/src/utils.py | 26 +- uv.lock | 108 ++++++- 10 files changed, 802 insertions(+), 249 deletions(-) diff --git a/README.md b/README.md index 65a294f..4790ad7 100644 --- a/README.md +++ b/README.md @@ -67,6 +67,8 @@ Get up and running in under 2 minutes: > **Prerequisites:** You must have QuantConnect credentials (User ID and API Token) before running the server. The server will not function without proper authentication. See [Authentication](#-authentication) section for details on obtaining these credentials. ### **Install with uvx (Recommended)** + +#### Core Installation (API Tools Only) ```bash # Install and run directly from PyPI - no cloning required! uvx quantconnect-mcp @@ -76,6 +78,16 @@ uv pip install quantconnect-mcp pip install quantconnect-mcp ``` +#### Full Installation (with QuantBook Support) +```bash +# Install with QuantBook container functionality +uv pip install "quantconnect-mcp[quantbook]" +pip install "quantconnect-mcp[quantbook]" + +# Requires Docker to be installed and running +docker --version # Ensure Docker is available +``` + ### One-Click Claude Desktop Install (Recommended) @@ -94,6 +106,9 @@ pip install quantconnect-mcp export QUANTCONNECT_USER_ID="your_user_id" # Required export QUANTCONNECT_API_TOKEN="your_api_token" # Required export QUANTCONNECT_ORGANIZATION_ID="your_org_id" # Optional + +# Optional: Enable QuantBook container functionality (default: false) +export ENABLE_QUANTBOOK="true" # Requires Docker + quantconnect-mcp[quantbook] ``` ### 3. **Launch the Server** @@ -101,11 +116,50 @@ export QUANTCONNECT_ORGANIZATION_ID="your_org_id" # Optional # STDIO transport (default) - Recommended for MCP clients uvx quantconnect-mcp +# With QuantBook functionality enabled +ENABLE_QUANTBOOK=true uvx quantconnect-mcp + # HTTP transport MCP_TRANSPORT=streamable-http MCP_PORT=8000 uvx quantconnect-mcp + +# Full configuration example +ENABLE_QUANTBOOK=true \ +LOG_LEVEL=DEBUG \ +MCP_TRANSPORT=streamable-http \ +MCP_PORT=8000 \ +uvx quantconnect-mcp ``` -### 4. **Interact with Natural Language** +### 4. **QuantBook Container Functionality (Optional)** + +The server supports optional QuantBook functionality that runs research environments in secure Docker containers. This provides: + +- **🐳 Containerized Execution**: Each QuantBook instance runs in an isolated Docker container +- **🔒 Enhanced Security**: Non-root users, capability dropping, resource limits +- **⚡ Scalable Sessions**: Multiple concurrent research sessions with automatic cleanup +- **📊 Interactive Analysis**: Execute Python code with full QuantConnect research libraries + +#### **Requirements** +- Docker installed and running +- Install with QuantBook support: `pip install "quantconnect-mcp[quantbook]"` +- Set environment variable: `ENABLE_QUANTBOOK=true` + +#### **Security Features** +- Containers run as non-root users (1000:1000) +- Network isolation (no external network access) +- Resource limits (configurable memory and CPU) +- Automatic session timeout and cleanup +- Code execution monitoring and logging + +#### **Container Configuration** +```bash +# Container resource limits (optional) +export QUANTBOOK_MEMORY_LIMIT="2g" # Default: 2GB RAM +export QUANTBOOK_CPU_LIMIT="1.0" # Default: 1 CPU core +export QUANTBOOK_SESSION_TIMEOUT="3600" # Default: 1 hour timeout +``` + +### 5. **Interact with Natural Language** Instead of calling tools programmatically, you use natural language with a connected AI client (like Claude, a GPT, or any other MCP-compatible interface). @@ -232,24 +286,26 @@ This MCP server is designed to be used with natural language. Below are examples | `update_file_content` | Update file content | `project_id`, `name`, `content` | | `update_file_name` | Rename file in project | `project_id`, `old_file_name`, `new_name` | -### ◆ QuantBook Research Tools +### ◆ QuantBook Research Tools (Optional - Requires ENABLE_QUANTBOOK=true) | Tool | Description | Key Parameters | |------|-------------|----------------| -| `initialize_quantbook` | Create new research instance | `instance_name`, `organization_id`, `token` | -| `list_quantbook_instances` | View all active instances | - | -| `get_quantbook_info` | Get instance details | `instance_name` | -| `remove_quantbook_instance` | Clean up instance | `instance_name` | +| `initialize_quantbook` | Create new containerized research instance | `instance_name`, `memory_limit`, `cpu_limit`, `timeout` | +| `list_quantbook_instances` | View all active container instances | - | +| `get_quantbook_info` | Get container instance details | `instance_name` | +| `remove_quantbook_instance` | Clean up container instance | `instance_name` | +| `execute_quantbook_code` | Execute Python code in container | `code`, `instance_name`, `timeout` | +| `get_session_manager_status` | Get container session manager status | - | -### ◆ Data Retrieval Tools +### ◆ Data Retrieval Tools (Optional - Requires ENABLE_QUANTBOOK=true) | Tool | Description | Key Parameters | |------|-------------|----------------| -| `add_equity` | Add single equity security | `ticker`, `resolution`, `instance_name` | -| `add_multiple_equities` | Add multiple securities | `tickers`, `resolution`, `instance_name` | -| `get_history` | Get historical price data | `symbols`, `start_date`, `end_date`, `resolution` | -| `add_alternative_data` | Subscribe to alt data | `data_type`, `symbol`, `instance_name` | -| `get_alternative_data_history` | Get alt data history | `data_type`, `symbols`, `start_date`, `end_date` | +| `add_equity` | Add single equity security to container | `ticker`, `resolution`, `instance_name` | +| `add_multiple_equities` | Add multiple securities to container | `tickers`, `resolution`, `instance_name` | +| `get_history` | Get historical price data in container | `symbols`, `start_date`, `end_date`, `resolution` | +| `add_alternative_data` | Subscribe to alt data in container | `data_type`, `symbol`, `instance_name` | +| `get_alternative_data_history` | Get alt data history in container | `data_type`, `symbols`, `start_date`, `end_date` | ### ◆ Statistical Analysis Tools @@ -331,6 +387,7 @@ quantconnect-mcp/ ### Environment Variables +#### Core Server Configuration | Variable | Description | Default | Example | |----------|-------------|---------|---------| | `MCP_TRANSPORT` | Transport method | `stdio` | `streamable-http` | @@ -338,6 +395,23 @@ quantconnect-mcp/ | `MCP_PORT` | Server port | `8000` | `3000` | | `MCP_PATH` | HTTP endpoint path | `/mcp` | `/api/v1/mcp` | | `LOG_LEVEL` | Logging verbosity | `INFO` | `DEBUG` | +| `LOG_FILE` | Log file path | None | `/var/log/quantconnect-mcp.log` | + +#### QuantConnect Authentication +| Variable | Description | Required | Example | +|----------|-------------|----------|---------| +| `QUANTCONNECT_USER_ID` | Your QuantConnect user ID | ◉ Yes | `123456` | +| `QUANTCONNECT_API_TOKEN` | Your QuantConnect API token | ◉ Yes | `abc123...` | +| `QUANTCONNECT_ORGANIZATION_ID` | Organization ID (optional) | ◦ No | `org123` | + +#### QuantBook Container Configuration (Optional) +| Variable | Description | Default | Example | +|----------|-------------|---------|---------| +| `ENABLE_QUANTBOOK` | Enable QuantBook functionality | `false` | `true` | +| `QUANTBOOK_MEMORY_LIMIT` | Container memory limit | `2g` | `4g` | +| `QUANTBOOK_CPU_LIMIT` | Container CPU limit | `1.0` | `2.0` | +| `QUANTBOOK_SESSION_TIMEOUT` | Session timeout (seconds) | `3600` | `7200` | +| `QUANTBOOK_MAX_SESSIONS` | Maximum concurrent sessions | `10` | `20` | ### System Resources diff --git a/pyproject.toml b/pyproject.toml index 2fcf784..7eb9c48 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,11 @@ dependencies = [ "quantconnect>=0.1.0", ] +[project.optional-dependencies] +quantbook = [ + "docker>=7.1.0", +] + [project.scripts] quantconnect-mcp = "quantconnect_mcp.main:main" diff --git a/quantconnect-mcp.dxt b/quantconnect-mcp.dxt index 365efaa092293653b966370680cfbda743dab1f2..0b6ccc8200f4889d6226ed38fb5a3b1ec30d13ef 100644 GIT binary patch delta 23751 zcmY&;Q*b3*({)a4+qP}n_Qampnw;3SolI=owryi#WB%uz@9MwmRcm!s?~Ao}Rd?^= zPpGVKCKBl18DyLfmDcJg#Q>!atqjh0LYkx zgYaLDE~)e7Iu|Gih*=VmG!}5u{*W2I>w^xSC`dOv=P|&Ly0I!`tUS7$zN#z#NduqG!77(sCq* zd6jJht5rcMOH#5Qj>mzrR$NuIsiN|v0BUy!?K&CV%o$*w@X}^{;y2xzOH)q2;`Iol zTnn$1>)Exk76jd@trzemXu=RryM}_wPOc?0KrsJ;G{x)0e~;zVk9dPJH4CC!nF#g) zv2Bq>oXN7F+{X!6aujeQFHcdeRn^ruHvb|i+LR^wvqdoM+d2CE3j`xh~jQ*1wFMNEtiw2Y~@HamvClwIOlu^H7$}=0~~F^alv4zh7>$v<_z|x zr}0(#CM1JN2EC-UM%Z4Dbx88g>>ttVW~bXJ%z=2QGWNP*xvDRVq0uZ}+G$MOGk>-3 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register_data_tools + return register_quantbook_tools, register_data_tools + except ImportError as e: + safe_print(f"⚠️ QuantBook dependencies not available: {e}") + return None, None + +def import_logging_setup(): + """Conditionally import logging setup.""" + try: + from quantconnect_mcp.src.adapters.logging_config import setup_logging + return setup_logging + except ImportError: + return None + + +# Global shutdown flag +_shutdown_requested = False +_session_manager: Optional[object] = None + + +def setup_signal_handlers() -> None: + """Setup signal handlers for graceful shutdown.""" + global _shutdown_requested + + def signal_handler(signum: int, frame) -> None: + global _shutdown_requested + if not _shutdown_requested: + _shutdown_requested = True + safe_print(f"\n🔄 Received signal {signum}, initiating graceful shutdown...") + + # Shutdown session manager if available + if _session_manager and hasattr(_session_manager, 'stop'): + try: + asyncio.create_task(_session_manager.stop()) + except Exception as e: + safe_print(f"⚠️ Error during session manager shutdown: {e}") + + # Register signal handlers + try: + signal.signal(signal.SIGINT, signal_handler) # Ctrl+C + signal.signal(signal.SIGTERM, signal_handler) # Termination + if hasattr(signal, 'SIGHUP'): + signal.signal(signal.SIGHUP, signal_handler) # Hangup + except Exception as e: + safe_print(f"⚠️ Could not setup signal handlers: {e}") + + +async def shutdown_cleanup() -> None: + """Perform cleanup during shutdown.""" + global _session_manager + + try: + if _session_manager and hasattr(_session_manager, 'stop'): + safe_print("🧹 Cleaning up session manager...") + await _session_manager.stop() + safe_print("✅ Session manager cleaned up") + except Exception as e: + safe_print(f"⚠️ Error during cleanup: {e}") + def main(): """Initialize and run the QuantConnect MCP server.""" + global _session_manager + + try: + # Setup signal handlers for graceful shutdown + setup_signal_handlers() + + # Check QuantBook support and environment configuration + enable_quantbook = os.getenv("ENABLE_QUANTBOOK", "false").lower() in ("true", "1", "yes", "on") + quantbook_available = check_quantbook_support() + + # Setup logging (basic logging if QuantBook not available) + log_level = os.getenv("LOG_LEVEL", "INFO") + log_file = os.getenv("LOG_FILE") + + # Setup advanced logging if available + setup_logging = import_logging_setup() + if setup_logging: + setup_logging( + log_level=log_level, + log_file=Path(log_file) if log_file else None, + include_container_logs=True, + ) + safe_print(f"🔧 Advanced logging configured (level: {log_level})") + else: + # Basic logging setup + logging.basicConfig( + level=getattr(logging, log_level.upper()), + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' + ) + safe_print(f"🔧 Basic logging configured (level: {log_level})") + + if quantbook_available: + register_quantbook_tools, register_data_tools = import_quantbook_modules() + else: + register_quantbook_tools = None + register_data_tools = None + + # Auto-configure authentication from environment variables if available + user_id = os.getenv("QUANTCONNECT_USER_ID") + api_token = os.getenv("QUANTCONNECT_API_TOKEN") + organization_id = os.getenv("QUANTCONNECT_ORGANIZATION_ID") + + if user_id and api_token: + try: + safe_print("🔐 Configuring QuantConnect authentication from environment...") + configure_auth(user_id, api_token, organization_id) + safe_print("✅ Authentication configured successfully") + except Exception as e: + safe_print(f"⚠️ Failed to configure authentication: {e}") + safe_print( + "💡 You can configure authentication later using the configure_quantconnect_auth tool" + ) + + # Register core tool modules (always available) + safe_print("🔧 Registering QuantConnect API tools...") + register_auth_tools(mcp) + register_project_tools(mcp) + register_file_tools(mcp) + register_backtest_tools(mcp) + register_analysis_tools(mcp) + register_portfolio_tools(mcp) + register_universe_tools(mcp) - # Auto-configure authentication from environment variables if available - user_id = os.getenv("QUANTCONNECT_USER_ID") - api_token = os.getenv("QUANTCONNECT_API_TOKEN") - organization_id = os.getenv("QUANTCONNECT_ORGANIZATION_ID") + # Conditionally register QuantBook tools + if enable_quantbook: + if quantbook_available and register_quantbook_tools and register_data_tools: + safe_print("🐳 Registering QuantBook container tools...") + register_quantbook_tools(mcp) + register_data_tools(mcp) + safe_print("✅ QuantBook functionality enabled") + + # Get session manager reference for cleanup + try: + from quantconnect_mcp.src.adapters.session_manager import get_session_manager + _session_manager = get_session_manager() + except ImportError: + pass + else: + safe_print("❌ QuantBook functionality requested but dependencies not available") + safe_print("💡 Install with: pip install quantconnect-mcp[quantbook]") + safe_print("🐳 Ensure Docker is installed and accessible") + else: + safe_print("⏭️ QuantBook functionality disabled (set ENABLE_QUANTBOOK=true to enable)") - if user_id and api_token: + # Register resources + safe_print("📊 Registering system resources...") + register_system_resources(mcp) + + safe_print(f"✅ QuantConnect MCP Server initialized") + + # Determine transport method + transport = os.getenv("MCP_TRANSPORT", "stdio") + + # Run server with proper error handling try: - safe_print("🔐 Configuring QuantConnect authentication from environment...") - configure_auth(user_id, api_token, organization_id) - safe_print("✅ Authentication configured successfully") + if transport == "streamable-http": + host = os.getenv("MCP_HOST", "0.0.0.0") + port = int(os.getenv("MCP_PORT", os.getenv("PORT", "8000"))) + safe_print(f"🌐 Starting HTTP server on {host}:{port}") + mcp.run( + transport="streamable-http", + host=host, + port=port, + path=os.getenv("MCP_PATH", "/mcp"), + ) + elif transport == "stdio": + safe_print("📡 Starting STDIO transport") + mcp.run() # Default stdio transport + else: + safe_print(f"🚀 Starting with {transport} transport") + mcp.run(transport=transport) + + except (BrokenPipeError, ConnectionResetError, EOFError) as e: + # Client disconnected - this is normal, not an error + safe_print("🔌 Client disconnected") + logging.getLogger(__name__).debug(f"Client disconnect: {e}") + + except KeyboardInterrupt: + safe_print("\n⏹️ Keyboard interrupt received") + + except OSError as e: + if e.errno == 32: # Broken pipe + safe_print("🔌 Client disconnected (broken pipe)") + logging.getLogger(__name__).debug(f"Broken pipe: {e}") + else: + safe_print(f"❌ OS Error: {e}") + raise + except Exception as e: - safe_print(f"⚠️ Failed to configure authentication: {e}") - safe_print( - "💡 You can configure authentication later using the configure_quantconnect_auth tool" - ) - - # Register all tool modules - safe_print("🔧 Registering QuantConnect tools...") - register_auth_tools(mcp) - register_project_tools(mcp) - register_file_tools(mcp) - register_backtest_tools(mcp) - register_quantbook_tools(mcp) - register_data_tools(mcp) - register_analysis_tools(mcp) - register_portfolio_tools(mcp) - register_universe_tools(mcp) - - # Register resources - safe_print("📊 Registering system resources...") - register_system_resources(mcp) - - safe_print(f"✅ QuantConnect MCP Server initialized") - - # Determine transport method - transport = os.getenv("MCP_TRANSPORT", "stdio") - - if transport == "streamable-http": - host = os.getenv("MCP_HOST", "0.0.0.0") - port = int(os.getenv("MCP_PORT", os.getenv("PORT", "8000"))) - safe_print(f"🌐 Starting HTTP server on {host}:{port}") - mcp.run( - transport="streamable-http", - host=host, - port=port, - path=os.getenv("MCP_PATH", "/mcp"), - ) - elif transport == "stdio": - safe_print("📡 Starting STDIO transport") - mcp.run() # Default stdio transport - else: - safe_print(f"🚀 Starting with {transport} transport") - mcp.run(transport=transport) + safe_print(f"❌ Unexpected error: {e}") + logging.getLogger(__name__).error(f"Server error: {e}", exc_info=True) + raise + + finally: + # Cleanup + if _session_manager: + try: + import asyncio + asyncio.run(shutdown_cleanup()) + except Exception as e: + safe_print(f"⚠️ Error during final cleanup: {e}") + + except KeyboardInterrupt: + safe_print("\n⏹️ Startup interrupted") + sys.exit(1) + + except Exception as e: + safe_print(f"❌ Failed to start server: {e}") + logging.getLogger(__name__).error(f"Startup error: {e}", exc_info=True) + sys.exit(1) + + safe_print("👋 QuantConnect MCP Server shutdown complete") if __name__ == "__main__": diff --git a/quantconnect_mcp/src/server.py b/quantconnect_mcp/src/server.py index 8dc6e84..1cd8e62 100644 --- a/quantconnect_mcp/src/server.py +++ b/quantconnect_mcp/src/server.py @@ -5,8 +5,6 @@ from fastmcp import FastMCP from .tools import ( - register_quantbook_tools, - register_data_tools, register_analysis_tools, register_portfolio_tools, register_universe_tools, @@ -23,15 +21,20 @@ mcp: FastMCP = FastMCP( name="QuantConnect MCP Server", instructions=""" - This server provides comprehensive QuantConnect API functionality for: - - Research environment operations with QuantBook - - Historical data retrieval and analysis + This server provides QuantConnect API functionality for: + - Project and backtest management - Statistical analysis (PCA, cointegration, mean reversion) - Portfolio optimization and risk analysis - Universe selection and asset filtering - Alternative data integration + - File management and organization + + Optional QuantBook functionality (requires ENABLE_QUANTBOOK=true): + - Research environment operations with QuantBook in Docker containers + - Historical data retrieval and analysis + - Interactive Jupyter-like code execution - Use the available tools to interact with QuantConnect's research capabilities. + Use the available tools to interact with QuantConnect's capabilities. """, on_duplicate_tools="error", dependencies=[ @@ -47,63 +50,4 @@ ], ) -def main(): - """Initialize and run the QuantConnect MCP server.""" - - # Auto-configure authentication from environment variables if available - user_id = os.getenv("QUANTCONNECT_USER_ID") - api_token = os.getenv("QUANTCONNECT_API_TOKEN") - organization_id = os.getenv("QUANTCONNECT_ORGANIZATION_ID") - - if user_id and api_token: - try: - safe_print("🔐 Configuring QuantConnect authentication from environment...") - configure_auth(user_id, api_token, organization_id) - safe_print("✅ Authentication configured successfully") - except Exception as e: - safe_print(f"⚠️ Failed to configure authentication: {e}") - safe_print( - "💡 You can configure authentication later using the configure_quantconnect_auth tool" - ) - - # Register all tool modules - safe_print("🔧 Registering QuantConnect tools...") - register_auth_tools(mcp) - register_project_tools(mcp) - register_file_tools(mcp) - register_backtest_tools(mcp) - register_quantbook_tools(mcp) - register_data_tools(mcp) - register_analysis_tools(mcp) - register_portfolio_tools(mcp) - register_universe_tools(mcp) - - # Register resources - safe_print("📊 Registering system resources...") - register_system_resources(mcp) - - safe_print(f"✅ QuantConnect MCP Server initialized") - - # Determine transport method - transport = os.getenv("MCP_TRANSPORT", "stdio") - - if transport == "streamable-http": - host = os.getenv("MCP_HOST", "127.0.0.1") - port = int(os.getenv("MCP_PORT", "8000")) - safe_print(f"🌐 Starting HTTP server on {host}:{port}") - mcp.run( - transport="streamable-http", - host=host, - port=port, - path=os.getenv("MCP_PATH", "/mcp"), - ) - elif transport == "stdio": - safe_print("📡 Starting STDIO transport") - mcp.run() # Default stdio transport - else: - safe_print(f"🚀 Starting with {transport} transport") - mcp.run(transport=transport) - - -if __name__ == "__main__": - main() +# Server configuration is now handled in main.py diff --git a/quantconnect_mcp/src/tools/__init__.py b/quantconnect_mcp/src/tools/__init__.py index 7ff77e8..15faf7e 100644 --- a/quantconnect_mcp/src/tools/__init__.py +++ b/quantconnect_mcp/src/tools/__init__.py @@ -1,7 +1,6 @@ """QuantConnect MCP Tools Package""" -from .quantbook_tools import register_quantbook_tools -from .data_tools import register_data_tools +# Core tools (always available) from .analysis_tools import register_analysis_tools from .portfolio_tools import register_portfolio_tools from .universe_tools import register_universe_tools @@ -10,9 +9,11 @@ from .file_tools import register_file_tools from .backtest_tools import register_backtest_tools +# QuantBook tools are imported conditionally in main.py to avoid Docker dependency +# from .quantbook_tools import register_quantbook_tools +# from .data_tools import register_data_tools + __all__ = [ - "register_quantbook_tools", - "register_data_tools", "register_analysis_tools", "register_portfolio_tools", "register_universe_tools", @@ -20,4 +21,7 @@ "register_project_tools", "register_file_tools", "register_backtest_tools", + # QuantBook tools excluded from __all__ - imported conditionally + # "register_quantbook_tools", + # "register_data_tools", ] diff --git a/quantconnect_mcp/src/tools/data_tools.py b/quantconnect_mcp/src/tools/data_tools.py index c8854f7..2cf273b 100644 --- a/quantconnect_mcp/src/tools/data_tools.py +++ b/quantconnect_mcp/src/tools/data_tools.py @@ -1,11 +1,14 @@ -"""Data Retrieval Tools for QuantConnect MCP Server""" +"""Data Retrieval Tools for QuantConnect MCP Server (Container-Based)""" from fastmcp import FastMCP from typing import Dict, Any, List, Optional, Union from datetime import datetime import pandas as pd import json -from .quantbook_tools import get_quantbook_instance +import logging +from .quantbook_tools import get_quantbook_session + +logger = logging.getLogger(__name__) def register_data_tools(mcp: FastMCP): @@ -26,40 +29,80 @@ async def add_equity( Returns: Dictionary containing the added security information """ - qb = get_quantbook_instance(instance_name) - if qb is None: + session = await get_quantbook_session(instance_name) + if session is None: return { "status": "error", "error": f"QuantBook instance '{instance_name}' not found", + "message": "Initialize a QuantBook instance first using initialize_quantbook", } try: - from QuantConnect import Resolution # type: ignore - - # Map string resolution to enum - resolution_map = { - "Minute": Resolution.Minute, - "Hour": Resolution.Hour, - "Daily": Resolution.Daily, - } - - if resolution not in resolution_map: + # Validate resolution + valid_resolutions = ["Minute", "Hour", "Daily"] + if resolution not in valid_resolutions: return { "status": "error", - "error": f"Invalid resolution '{resolution}'. Must be one of: {list(resolution_map.keys())}", + "error": f"Invalid resolution '{resolution}'. Must be one of: {valid_resolutions}", } - symbol = qb.AddEquity(ticker, resolution_map[resolution]).Symbol + # Execute code to add equity in container + add_equity_code = f""" +from QuantConnect import Resolution + +# Map string resolution to enum +resolution_map = {{ + "Minute": Resolution.Minute, + "Hour": Resolution.Hour, + "Daily": Resolution.Daily, +}} + +try: + # Add equity to QuantBook + security = qb.AddEquity("{ticker}", resolution_map["{resolution}"]) + symbol = str(security.Symbol) + + print(f"Successfully added equity '{ticker}' with {resolution} resolution") + print(f"Symbol: {{symbol}}") + + # Store result for return + result = {{ + "ticker": "{ticker}", + "symbol": symbol, + "resolution": "{resolution}", + "success": True + }} + +except Exception as e: + print(f"Failed to add equity '{ticker}': {{e}}") + result = {{ + "ticker": "{ticker}", + "error": str(e), + "success": False + }} +""" + + execution_result = await session.execute(add_equity_code) + + if execution_result["status"] != "success": + return { + "status": "error", + "error": execution_result.get("error", "Unknown error"), + "message": f"Failed to add equity '{ticker}'", + "execution_output": execution_result.get("output", ""), + } return { "status": "success", "ticker": ticker, - "symbol": str(symbol), "resolution": resolution, "message": f"Successfully added equity '{ticker}' with {resolution} resolution", + "execution_output": execution_result.get("output", ""), + "instance_name": instance_name, } except Exception as e: + logger.error(f"Failed to add equity '{ticker}' in instance '{instance_name}': {e}") return { "status": "error", "error": str(e), @@ -81,52 +124,84 @@ async def add_multiple_equities( Returns: Dictionary containing results for all added securities """ - qb = get_quantbook_instance(instance_name) - if qb is None: + session = await get_quantbook_session(instance_name) + if session is None: return { "status": "error", "error": f"QuantBook instance '{instance_name}' not found", + "message": "Initialize a QuantBook instance first using initialize_quantbook", } try: - from QuantConnect import Resolution # type: ignore - - resolution_map = { - "Minute": Resolution.Minute, - "Hour": Resolution.Hour, - "Daily": Resolution.Daily, - } - - if resolution not in resolution_map: + # Validate resolution + valid_resolutions = ["Minute", "Hour", "Daily"] + if resolution not in valid_resolutions: return { "status": "error", - "error": f"Invalid resolution '{resolution}'. Must be one of: {list(resolution_map.keys())}", + "error": f"Invalid resolution '{resolution}'. Must be one of: {valid_resolutions}", } - results = [] - symbols = {} - - for ticker in tickers: - try: - symbol = qb.AddEquity(ticker, resolution_map[resolution]).Symbol - symbols[ticker] = str(symbol) - results.append( - {"ticker": ticker, "symbol": str(symbol), "status": "success"} - ) - except Exception as e: - results.append( - {"ticker": ticker, "status": "error", "error": str(e)} - ) + # Convert tickers list to Python code representation + tickers_str = str(tickers) + + # Execute code to add multiple equities in container + add_multiple_code = f""" +from QuantConnect import Resolution + +# Map string resolution to enum +resolution_map = {{ + "Minute": Resolution.Minute, + "Hour": Resolution.Hour, + "Daily": Resolution.Daily, +}} + +tickers = {tickers_str} +resolution = "{resolution}" +results = [] +symbols = {{}} + +for ticker in tickers: + try: + security = qb.AddEquity(ticker, resolution_map[resolution]) + symbol = str(security.Symbol) + symbols[ticker] = symbol + results.append({{ + "ticker": ticker, + "symbol": symbol, + "status": "success" + }}) + print(f"Added equity {{ticker}} with symbol {{symbol}}") + except Exception as e: + results.append({{ + "ticker": ticker, + "status": "error", + "error": str(e) + }}) + print(f"Failed to add equity {{ticker}}: {{e}}") + +print(f"Successfully added {{len([r for r in results if r['status'] == 'success'])}} out of {{len(tickers)}} equities") +""" + + execution_result = await session.execute(add_multiple_code) + + if execution_result["status"] != "success": + return { + "status": "error", + "error": execution_result.get("error", "Unknown error"), + "message": "Failed to add multiple equities", + "execution_output": execution_result.get("output", ""), + } return { "status": "success", "resolution": resolution, - "symbols": symbols, - "results": results, - "total_added": len([r for r in results if r["status"] == "success"]), + "message": f"Processed {len(tickers)} equities", + "execution_output": execution_result.get("output", ""), + "instance_name": instance_name, } except Exception as e: + logger.error(f"Failed to add multiple equities in instance '{instance_name}': {e}") return { "status": "error", "error": str(e), diff --git a/quantconnect_mcp/src/tools/quantbook_tools.py b/quantconnect_mcp/src/tools/quantbook_tools.py index d6bed83..8ae33eb 100644 --- a/quantconnect_mcp/src/tools/quantbook_tools.py +++ b/quantconnect_mcp/src/tools/quantbook_tools.py @@ -1,11 +1,14 @@ -"""QuantBook Management Tools for QuantConnect MCP Server""" +"""QuantBook Management Tools for QuantConnect MCP Server (Container-Based)""" +import asyncio from fastmcp import FastMCP from typing import Dict, Any, List, Optional import json +import logging -# Global QuantBook instance storage -_quantbook_instances: Dict[str, Any] = {} +from ..adapters import SessionManager, ResearchSession, get_session_manager, initialize_session_manager + +logger = logging.getLogger(__name__) def register_quantbook_tools(mcp: FastMCP): @@ -16,42 +19,74 @@ async def initialize_quantbook( instance_name: str = "default", organization_id: Optional[str] = None, token: Optional[str] = None, + memory_limit: str = "2g", + cpu_limit: float = 1.0, + timeout: int = 300, ) -> Dict[str, Any]: """ - Initialize a new QuantBook instance for research operations. + Initialize a new QuantBook instance in a Docker container for research operations. Args: instance_name: Name identifier for this QuantBook instance - organization_id: Optional organization ID for QuantConnect - token: Optional API token for QuantConnect + organization_id: Optional organization ID for QuantConnect (not used in container) + token: Optional API token for QuantConnect (not used in container) + memory_limit: Container memory limit (e.g., "2g", "512m") + cpu_limit: Container CPU limit (fraction of CPU, e.g. 1.0 = 1 CPU) + timeout: Default execution timeout in seconds Returns: Dictionary containing initialization status and instance info """ try: - # Import QuantConnect modules - from QuantConnect.Research import QuantBook # type: ignore + # Initialize session manager if needed + await initialize_session_manager() + manager = get_session_manager() + + # Create or get research session + session = await manager.get_or_create_session( + session_id=instance_name, + memory_limit=memory_limit, + cpu_limit=cpu_limit, + timeout=timeout, + ) - # Create new QuantBook instance - qb = QuantBook() + # Initialize QuantBook in the container + init_code = """ +from QuantConnect.Research import QuantBook +import pandas as pd +import numpy as np - # Store the instance - _quantbook_instances[instance_name] = qb +# Create global QuantBook instance +qb = QuantBook() +print(f"QuantBook initialized successfully") +print(f"Available methods: {len([m for m in dir(qb) if not m.startswith('_')]):d}") +""" + + result = await session.execute(init_code) + + if result["status"] != "success": + return { + "status": "error", + "error": result.get("error", "Unknown error"), + "message": f"Failed to initialize QuantBook in container for instance '{instance_name}'", + } return { "status": "success", "instance_name": instance_name, - "message": f"QuantBook instance '{instance_name}' initialized successfully", - "available_instances": list(_quantbook_instances.keys()), + "session_id": session.session_id, + "message": f"QuantBook instance '{instance_name}' initialized successfully in container", + "container_info": { + "memory_limit": memory_limit, + "cpu_limit": cpu_limit, + "timeout": timeout, + "workspace": str(session.workspace_dir), + }, + "output": result.get("output", ""), } - except ImportError as e: - return { - "status": "error", - "error": f"Failed to import QuantConnect modules: {str(e)}", - "message": "Ensure QuantConnect LEAN is properly installed", - } except Exception as e: + logger.error(f"Failed to initialize QuantBook instance '{instance_name}': {e}") return { "status": "error", "error": str(e), @@ -66,11 +101,24 @@ async def list_quantbook_instances() -> Dict[str, Any]: Returns: Dictionary containing all active QuantBook instances """ - return { - "instances": list(_quantbook_instances.keys()), - "count": len(_quantbook_instances), - "status": "success", - } + try: + manager = get_session_manager() + sessions = manager.list_sessions() + session_count = manager.get_session_count() + + return { + "instances": [s["session_id"] for s in sessions], + "count": len(sessions), + "session_details": sessions, + "capacity": session_count, + "status": "success", + } + except Exception as e: + return { + "status": "error", + "error": str(e), + "message": "Failed to list QuantBook instances", + } @mcp.tool() async def get_quantbook_info(instance_name: str = "default") -> Dict[str, Any]: @@ -83,30 +131,68 @@ async def get_quantbook_info(instance_name: str = "default") -> Dict[str, Any]: Returns: Dictionary containing instance information """ - if instance_name not in _quantbook_instances: - return { - "status": "error", - "error": f"QuantBook instance '{instance_name}' not found", - "available_instances": list(_quantbook_instances.keys()), - } - try: - qb = _quantbook_instances[instance_name] + manager = get_session_manager() + session = await manager.get_session(instance_name) - # Get basic info about the instance - securities_count = len(qb.Securities) if hasattr(qb, "Securities") else 0 + if session is None: + available_sessions = [s["session_id"] for s in manager.list_sessions()] + return { + "status": "error", + "error": f"QuantBook instance '{instance_name}' not found", + "available_instances": available_sessions, + } + # Get QuantBook info from container + info_code = """ +try: + # Get securities count + securities_count = len(qb.Securities) if hasattr(qb, 'Securities') else 0 + + # Get available methods + available_methods = [method for method in dir(qb) if not method.startswith('_')] + + print(f"Securities count: {securities_count}") + print(f"Available methods: {len(available_methods)}") + print(f"QuantBook type: {type(qb).__name__}") + + # Store results for JSON return + qb_info = { + 'securities_count': securities_count, + 'available_methods': available_methods[:50], # Limit to first 50 methods + 'total_methods': len(available_methods), + 'type': type(qb).__name__ + } + +except Exception as e: + print(f"Error getting QuantBook info: {e}") + qb_info = { + 'error': str(e), + 'securities_count': 0, + 'available_methods': [], + 'total_methods': 0, + 'type': 'Unknown' + } +""" + + result = await session.execute(info_code) + return { "status": "success", "instance_name": instance_name, - "securities_count": securities_count, - "type": str(type(qb).__name__), - "available_methods": [ - method for method in dir(qb) if not method.startswith("_") - ], + "session_id": session.session_id, + "container_info": { + "created_at": session.created_at.isoformat(), + "last_used": session.last_used.isoformat(), + "memory_limit": session.memory_limit, + "cpu_limit": session.cpu_limit, + "workspace": str(session.workspace_dir), + }, + "execution_result": result, } except Exception as e: + logger.error(f"Failed to get info for QuantBook instance '{instance_name}': {e}") return { "status": "error", "error": str(e), @@ -116,7 +202,7 @@ async def get_quantbook_info(instance_name: str = "default") -> Dict[str, Any]: @mcp.tool() async def remove_quantbook_instance(instance_name: str) -> Dict[str, Any]: """ - Remove a QuantBook instance from memory. + Remove a QuantBook instance and clean up its container. Args: instance_name: Name of the QuantBook instance to remove @@ -124,28 +210,124 @@ async def remove_quantbook_instance(instance_name: str) -> Dict[str, Any]: Returns: Dictionary containing removal status """ - if instance_name not in _quantbook_instances: + try: + manager = get_session_manager() + success = await manager.close_session(instance_name) + + if not success: + available_sessions = [s["session_id"] for s in manager.list_sessions()] + return { + "status": "error", + "error": f"QuantBook instance '{instance_name}' not found", + "available_instances": available_sessions, + } + + remaining_sessions = [s["session_id"] for s in manager.list_sessions()] + return { + "status": "success", + "message": f"QuantBook instance '{instance_name}' removed successfully", + "remaining_instances": remaining_sessions, + } + + except Exception as e: + logger.error(f"Failed to remove QuantBook instance '{instance_name}': {e}") return { "status": "error", - "error": f"QuantBook instance '{instance_name}' not found", - "available_instances": list(_quantbook_instances.keys()), + "error": str(e), + "message": f"Failed to remove QuantBook instance '{instance_name}'", } + @mcp.tool() + async def execute_quantbook_code( + code: str, + instance_name: str = "default", + timeout: Optional[int] = None, + ) -> Dict[str, Any]: + """ + Execute arbitrary Python code in a QuantBook container. + + Args: + code: Python code to execute + instance_name: Name of the QuantBook instance + timeout: Execution timeout in seconds (uses session default if None) + + Returns: + Dictionary containing execution results + """ try: - del _quantbook_instances[instance_name] + manager = get_session_manager() + session = await manager.get_session(instance_name) + + if session is None: + available_sessions = [s["session_id"] for s in manager.list_sessions()] + return { + "status": "error", + "error": f"QuantBook instance '{instance_name}' not found", + "available_instances": available_sessions, + "message": "Initialize a QuantBook instance first using initialize_quantbook", + } + + # Execute the code + result = await session.execute(code, timeout=timeout) + result["instance_name"] = instance_name + + return result + + except Exception as e: + logger.error(f"Failed to execute code in QuantBook instance '{instance_name}': {e}") + return { + "status": "error", + "error": str(e), + "message": f"Failed to execute code in QuantBook instance '{instance_name}'", + "instance_name": instance_name, + } + + @mcp.tool() + async def get_session_manager_status() -> Dict[str, Any]: + """ + Get status information about the session manager. + + Returns: + Dictionary containing session manager status + """ + try: + manager = get_session_manager() + session_count = manager.get_session_count() + sessions = manager.list_sessions() + return { "status": "success", - "message": f"QuantBook instance '{instance_name}' removed successfully", - "remaining_instances": list(_quantbook_instances.keys()), + "running": manager._running, + "session_count": session_count, + "sessions": sessions, + "configuration": { + "max_sessions": manager.max_sessions, + "session_timeout_hours": manager.session_timeout.total_seconds() / 3600, + "cleanup_interval_seconds": manager.cleanup_interval, + }, } + except Exception as e: return { "status": "error", "error": str(e), - "message": f"Failed to remove QuantBook instance '{instance_name}'", + "message": "Failed to get session manager status", } -def get_quantbook_instance(instance_name: str = "default"): - """Helper function to get QuantBook instance for other tools.""" - return _quantbook_instances.get(instance_name) +async def get_quantbook_session(instance_name: str = "default") -> Optional[ResearchSession]: + """ + Helper function to get QuantBook session for other tools. + + Args: + instance_name: Name of the QuantBook instance + + Returns: + ResearchSession instance or None if not found + """ + try: + manager = get_session_manager() + return await manager.get_session(instance_name) + except Exception as e: + logger.error(f"Failed to get QuantBook session '{instance_name}': {e}") + return None \ No newline at end of file diff --git a/quantconnect_mcp/src/utils.py b/quantconnect_mcp/src/utils.py index f998a5b..477e734 100644 --- a/quantconnect_mcp/src/utils.py +++ b/quantconnect_mcp/src/utils.py @@ -7,7 +7,7 @@ def safe_print(text): - """Print text safely, handling emojis and MCP server context. + """Print text safely, handling emojis, broken pipes, and MCP server context. 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quantconnect_mcp/src/tools/analysis_tools.py | 10 ++++- quantconnect_mcp/src/tools/portfolio_tools.py | 10 ++++- quantconnect_mcp/src/tools/quantbook_tools.py | 14 +++++- quantconnect_mcp/src/tools/universe_tools.py | 10 ++++- uv.lock | 20 ++++++++- 7 files changed, 85 insertions(+), 25 deletions(-) diff --git a/quantconnect_mcp/main.py b/quantconnect_mcp/main.py index 81a8e2f..9d3befd 100644 --- a/quantconnect_mcp/main.py +++ b/quantconnect_mcp/main.py @@ -40,7 +40,8 @@ def check_quantbook_support() -> bool: def import_quantbook_modules(): """Conditionally import QuantBook modules.""" try: - from quantconnect_mcp.src.tools import register_quantbook_tools, register_data_tools + from quantconnect_mcp.src.tools.quantbook_tools import register_quantbook_tools + from quantconnect_mcp.src.tools.data_tools import register_data_tools return register_quantbook_tools, register_data_tools except ImportError as e: safe_print(f"⚠️ QuantBook dependencies not available: {e}") diff --git a/quantconnect_mcp/src/resources/system_resources.py b/quantconnect_mcp/src/resources/system_resources.py index 8f6741c..1b93639 100644 --- a/quantconnect_mcp/src/resources/system_resources.py +++ b/quantconnect_mcp/src/resources/system_resources.py @@ -39,25 +39,31 @@ async def system_info() -> Dict[str, Any]: @mcp.resource("resource://quantconnect/server/status") async def server_status() -> Dict[str, Any]: """Get QuantConnect MCP server status and statistics.""" - from ..tools.quantbook_tools import _quantbook_instances # type: ignore - - # Count active QuantBook instances - active_instances = len(_quantbook_instances) - - # Get instance details + + # Try to get session manager status without causing import issues + active_instances = 0 instance_details = {} - for name, qb in _quantbook_instances.items(): - try: - securities_count = ( - len(qb.Securities) if hasattr(qb, "Securities") else 0 - ) - instance_details[name] = { - "type": str(type(qb).__name__), - "securities_count": securities_count, + + try: + # Only try to import session manager if quantbook is available + from ..adapters.session_manager import get_session_manager + manager = get_session_manager() + sessions = manager.list_sessions() + active_instances = len(sessions) + + for session_info in sessions: + instance_details[session_info["session_id"]] = { + "type": "ResearchSession", "status": "active", + "created_at": session_info["created_at"], + "workspace": session_info["workspace_dir"], } - except Exception as e: - instance_details[name] = {"status": "error", "error": str(e)} + except ImportError: + # QuantBook functionality not available - that's okay + pass + except Exception as e: + # Other errors in session management + instance_details["error"] = str(e) return { "server_name": "QuantConnect MCP Server", @@ -65,8 +71,9 @@ async def server_status() -> Dict[str, Any]: "active_quantbook_instances": active_instances, "instance_details": instance_details, "available_tools": [ - "QuantBook Management", - "Data Retrieval", + "QuantConnect API", + "Project Management", + "Backtesting", "Statistical Analysis", "Portfolio Optimization", "Universe Selection", diff --git a/quantconnect_mcp/src/tools/analysis_tools.py b/quantconnect_mcp/src/tools/analysis_tools.py index 85eaffd..79df0ce 100644 --- a/quantconnect_mcp/src/tools/analysis_tools.py +++ b/quantconnect_mcp/src/tools/analysis_tools.py @@ -5,7 +5,15 @@ import pandas as pd import numpy as np import json -from .quantbook_tools import get_quantbook_instance + +# Conditional import to avoid issues when Docker/QuantBook not available +def get_quantbook_instance(instance_name: str = "default"): + """Get QuantBook instance - always returns None when quantbook unavailable.""" + try: + from .quantbook_tools import get_quantbook_instance as _get_instance + return _get_instance(instance_name) + except ImportError: + return None def register_analysis_tools(mcp: FastMCP): diff --git a/quantconnect_mcp/src/tools/portfolio_tools.py b/quantconnect_mcp/src/tools/portfolio_tools.py index 79d952d..820791c 100644 --- a/quantconnect_mcp/src/tools/portfolio_tools.py +++ b/quantconnect_mcp/src/tools/portfolio_tools.py @@ -5,7 +5,15 @@ import pandas as pd import numpy as np import json -from .quantbook_tools import get_quantbook_instance + +# Conditional import to avoid issues when Docker/QuantBook not available +def get_quantbook_instance(instance_name: str = "default"): + """Get QuantBook instance - always returns None when quantbook unavailable.""" + try: + from .quantbook_tools import get_quantbook_instance as _get_instance + return _get_instance(instance_name) + except ImportError: + return None def register_portfolio_tools(mcp: FastMCP): diff --git a/quantconnect_mcp/src/tools/quantbook_tools.py b/quantconnect_mcp/src/tools/quantbook_tools.py index 8ae33eb..827dc42 100644 --- a/quantconnect_mcp/src/tools/quantbook_tools.py +++ b/quantconnect_mcp/src/tools/quantbook_tools.py @@ -330,4 +330,16 @@ async def get_quantbook_session(instance_name: str = "default") -> Optional[Rese return await manager.get_session(instance_name) except Exception as e: logger.error(f"Failed to get QuantBook session '{instance_name}': {e}") - return None \ No newline at end of file + return None + + +def get_quantbook_instance(instance_name: str = "default"): + """ + Legacy compatibility function for get_quantbook_instance. + Returns None since the old synchronous API is no longer supported. + + This function exists to prevent import errors but will return None, + causing tools that depend on it to fail gracefully. + """ + logger.warning(f"get_quantbook_instance is deprecated and no longer functional. Use get_quantbook_session instead.") + return None \ No newline at end of file diff --git a/quantconnect_mcp/src/tools/universe_tools.py b/quantconnect_mcp/src/tools/universe_tools.py index fa8e03b..eeac04d 100644 --- a/quantconnect_mcp/src/tools/universe_tools.py +++ b/quantconnect_mcp/src/tools/universe_tools.py @@ -5,7 +5,15 @@ import pandas as pd import numpy as np from datetime import datetime -from .quantbook_tools import get_quantbook_instance + +# Conditional import to avoid issues when Docker/QuantBook not available +def get_quantbook_instance(instance_name: str = "default"): + """Get QuantBook instance - always returns None when quantbook unavailable.""" + try: + from .quantbook_tools import get_quantbook_instance as _get_instance + return _get_instance(instance_name) + except ImportError: + return None async def _get_etf_constituents_helper( diff --git a/uv.lock b/uv.lock index 4c49a06..7d2208f 100644 --- a/uv.lock +++ b/uv.lock @@ -1011,6 +1011,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446, upload_time = "2024-08-06T20:33:04.33Z" }, ] +[[package]] +name = "quantconnect" +version = "0.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/11/2a/2762a4e3497d35830ac5122fd2bcb7f5fb996f7c8ea47a255c03378466ff/quantconnect-0.1.0.tar.gz", hash = "sha256:9c47411e925141112b40893e0ae1b9364e63b487ce710322cb031d57e022ffd2", size = 921, upload_time = "2020-06-19T21:59:54.104Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/26/30/6624de8b559a496cf34957c4bdb25622713754bcbdfefc06812ed841e92f/quantconnect-0.1.0-py3-none-any.whl", hash = "sha256:c134dcfaf628066932984bc28377c4d717658af108fc70e1f603e5c63856be15", size = 5313, upload_time = "2020-06-19T21:59:52.096Z" }, +] + [[package]] name = "quantconnect-lean" version = "0.1.0" @@ -1026,7 +1035,6 @@ version = "0.1.11" source = { editable = "." } dependencies = [ { name = "arch" }, - { name = "docker" }, { name = "fastmcp" }, { name = "httpx" }, { name = "matplotlib" }, @@ -1034,6 +1042,7 @@ dependencies = [ { name = "pandas" }, { name = "psutil" }, { name = "pytest-asyncio" }, + { name = "quantconnect" }, { name = "quantconnect-lean" }, { name = "scikit-learn" }, { name = "scipy" }, @@ -1041,6 +1050,11 @@ dependencies = [ { name = "statsmodels" }, ] +[package.optional-dependencies] +quantbook = [ + { name = "docker" }, +] + [package.dev-dependencies] dev = [ { name = "black" }, @@ -1055,7 +1069,7 @@ dev = [ [package.metadata] requires-dist = [ { name = "arch", specifier = ">=7.2.0" }, - { name = "docker", specifier = ">=7.1.0" }, + { name = "docker", marker = "extra == 'quantbook'", specifier = ">=7.1.0" }, { name = "fastmcp", specifier = ">=2.7.1" }, { name = "httpx", specifier = ">=0.28.1" }, { name = "matplotlib", specifier = ">=3.10.3" }, @@ -1063,12 +1077,14 @@ requires-dist = [ { name = "pandas", specifier = ">=2.3.0" }, { name = "psutil", specifier = ">=7.0.0" }, { name = "pytest-asyncio", specifier = ">=1.0.0" }, + { name = "quantconnect", specifier = ">=0.1.0" }, { name = "quantconnect-lean" }, { name = "scikit-learn", specifier = ">=1.7.0" }, { name = "scipy", specifier = ">=1.15.3" }, { name = "seaborn", specifier = ">=0.13.2" }, { name = "statsmodels", specifier = ">=0.14.4" }, ] +provides-extras = ["quantbook"] [package.metadata.requires-dev] dev = [ From 1c7dfaa1f547632047dc305d5db06a167c632c19 Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Thu, 24 Jul 2025 17:45:30 -0400 Subject: [PATCH 03/11] working creation & retrieval --- quantconnect_mcp/src/adapters/__init__.py | 7 + .../src/adapters/logging_config.py | 119 +++++ .../src/adapters/research_session.py | 501 ++++++++++++++++++ .../src/adapters/session_manager.py | 247 +++++++++ quantconnect_mcp/src/tools/quantbook_tools.py | 26 +- 5 files changed, 894 insertions(+), 6 deletions(-) create mode 100644 quantconnect_mcp/src/adapters/__init__.py create mode 100644 quantconnect_mcp/src/adapters/logging_config.py create mode 100644 quantconnect_mcp/src/adapters/research_session.py create mode 100644 quantconnect_mcp/src/adapters/session_manager.py diff --git a/quantconnect_mcp/src/adapters/__init__.py b/quantconnect_mcp/src/adapters/__init__.py new file mode 100644 index 0000000..0a49d65 --- /dev/null +++ b/quantconnect_mcp/src/adapters/__init__.py @@ -0,0 +1,7 @@ +"""Adapter modules for external integrations.""" + +from .research_session import ResearchSession +from .session_manager import SessionManager, get_session_manager, initialize_session_manager +from .logging_config import setup_logging, security_logger + +__all__ = ["ResearchSession", "SessionManager", "get_session_manager", "initialize_session_manager", "setup_logging", "security_logger"] \ No newline at end of file diff --git a/quantconnect_mcp/src/adapters/logging_config.py b/quantconnect_mcp/src/adapters/logging_config.py new file mode 100644 index 0000000..5a8d042 --- /dev/null +++ b/quantconnect_mcp/src/adapters/logging_config.py @@ -0,0 +1,119 @@ +"""Logging configuration for QuantConnect MCP Server""" + +import logging +import sys +from datetime import datetime +from pathlib import Path +from typing import Optional + + +def setup_logging( + log_level: str = "INFO", + log_file: Optional[Path] = None, + include_container_logs: bool = True, +) -> None: + """ + Setup logging configuration for the MCP server. + + Args: + log_level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) + log_file: Optional log file path + include_container_logs: Whether to include container-specific logging + """ + + # Create formatters + detailed_formatter = logging.Formatter( + fmt='%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' + ) + + simple_formatter = logging.Formatter( + fmt='%(asctime)s - %(levelname)s - %(message)s', + datefmt='%H:%M:%S' + ) + + # Setup root logger + root_logger = logging.getLogger() + root_logger.setLevel(getattr(logging, log_level.upper())) + + # Clear existing handlers + root_logger.handlers.clear() + + # Console handler - MUST use stderr to avoid contaminating MCP JSON-RPC on stdout + console_handler = logging.StreamHandler(sys.stderr) + console_handler.setLevel(logging.INFO) + console_handler.setFormatter(simple_formatter) + root_logger.addHandler(console_handler) + + # File handler if specified + if log_file: + log_file.parent.mkdir(parents=True, exist_ok=True) + file_handler = logging.FileHandler(log_file) + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(detailed_formatter) + root_logger.addHandler(file_handler) + + # Setup specific loggers with appropriate levels + loggers = { + 'quantconnect_mcp': logging.DEBUG, + 'quantconnect_mcp.adapters': logging.DEBUG, + 'quantconnect_mcp.adapters.research_session': logging.INFO, + 'quantconnect_mcp.adapters.session_manager': logging.INFO, + 'quantconnect_mcp.tools': logging.INFO, + 'docker': logging.WARNING, # Reduce noise from Docker client + 'urllib3': logging.WARNING, # Reduce noise from HTTP requests + } + + for logger_name, level in loggers.items(): + logger = logging.getLogger(logger_name) + logger.setLevel(level) + + # Log startup message + root_logger.info(f"Logging initialized - Level: {log_level}, File: {log_file}") + + +def get_container_logger(session_id: str) -> logging.Logger: + """Get a logger specific to a container session.""" + return logging.getLogger(f"quantconnect_mcp.container.{session_id}") + + +class SecurityLogger: + """Logger for security-related events.""" + + def __init__(self): + self.logger = logging.getLogger("quantconnect_mcp.security") + + def log_session_created(self, session_id: str, container_id: str) -> None: + """Log session creation.""" + self.logger.info( + f"SECURITY: Session created - ID: {session_id}, Container: {container_id}" + ) + + def log_session_destroyed(self, session_id: str, reason: str = "normal") -> None: + """Log session destruction.""" + self.logger.info( + f"SECURITY: Session destroyed - ID: {session_id}, Reason: {reason}" + ) + + def log_code_execution(self, session_id: str, code_hash: str, success: bool) -> None: + """Log code execution attempts.""" + status = "SUCCESS" if success else "FAILED" + self.logger.info( + f"SECURITY: Code execution {status} - Session: {session_id}, Hash: {code_hash}" + ) + + def log_security_violation(self, session_id: str, violation_type: str, details: str) -> None: + """Log security violations.""" + self.logger.warning( + f"SECURITY VIOLATION: {violation_type} - Session: {session_id}, Details: {details}" + ) + + def log_resource_limit_hit(self, session_id: str, resource: str, limit: str) -> None: + """Log when resource limits are hit.""" + self.logger.warning( + f"SECURITY: Resource limit hit - Session: {session_id}, Resource: {resource}, Limit: {limit}" + ) + + +# Global security logger instance +security_logger = SecurityLogger() \ No newline at end of file diff --git a/quantconnect_mcp/src/adapters/research_session.py b/quantconnect_mcp/src/adapters/research_session.py new file mode 100644 index 0000000..6dc66d3 --- /dev/null +++ b/quantconnect_mcp/src/adapters/research_session.py @@ -0,0 +1,501 @@ +"""QuantConnect Research Session Container Adapter""" + +import asyncio +import hashlib +import json +import logging +import tempfile +import uuid +from datetime import datetime, timedelta +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +import docker +import docker.types +import pandas as pd +from docker.models.containers import Container + +from .logging_config import get_container_logger, security_logger + +logger = logging.getLogger(__name__) + + +class ResearchSessionError(Exception): + """Custom exception for research session errors.""" + pass + + +class ResearchSession: + """ + Container-based QuantConnect Research session adapter. + + Manages a Docker container running the quantconnect/research image + and provides methods to execute code and exchange data. + """ + + IMAGE = "quantconnect/research:latest" # Use DEFAULT_RESEARCH_IMAGE from lean constants + CONTAINER_WORKSPACE = "/Lean" # Match LEAN_ROOT_PATH + NOTEBOOKS_PATH = "/Lean/Notebooks" + TIMEOUT_DEFAULT = 300 # 5 minutes + + def __init__( + self, + session_id: Optional[str] = None, + workspace_dir: Optional[Path] = None, + memory_limit: str = "2g", + cpu_limit: float = 1.0, + timeout: int = TIMEOUT_DEFAULT, + ): + """ + Initialize a new research session. + + Args: + session_id: Unique identifier for this session + workspace_dir: Local workspace directory (temp dir if None) + memory_limit: Container memory limit (e.g., "2g", "512m") + cpu_limit: Container CPU limit (fraction of CPU) + timeout: Default execution timeout in seconds + """ + self.session_id = session_id or f"qb_{uuid.uuid4().hex[:8]}" + self.memory_limit = memory_limit + self.cpu_limit = cpu_limit + self.timeout = timeout + self.created_at = datetime.utcnow() + self.last_used = self.created_at + + # Setup workspace + if workspace_dir: + self.workspace_dir = Path(workspace_dir) + self.workspace_dir.mkdir(parents=True, exist_ok=True) + self._temp_dir = None + else: + self._temp_dir = tempfile.TemporaryDirectory(prefix=f"qc_research_{self.session_id}_") + self.workspace_dir = Path(self._temp_dir.name) + + # Docker client and container + self.client = docker.from_env() + self.container: Optional[Container] = None + self._initialized = False + + logger.info(f"Created research session {self.session_id}") + + async def initialize(self) -> None: + """Initialize the Docker container.""" + if self._initialized: + return + + try: + # Ensure the image is available + try: + self.client.images.get(self.IMAGE) + except docker.errors.ImageNotFound: + logger.info(f"Pulling image {self.IMAGE}...") + self.client.images.pull(self.IMAGE) + + # Start container with proper LEAN research environment + # Use similar approach to lean-cli research command + volumes = { + str(self.workspace_dir): { + "bind": self.NOTEBOOKS_PATH, + "mode": "rw" + } + } + + # Add environment variables like lean-cli does + environment = { + "COMPOSER_DLL_DIRECTORY": "/Lean", + "LEAN_ENGINE": "true", + "PYTHONPATH": "/Lean" + } + + # Create container with simplified configuration to ensure it starts + try: + self.container = self.client.containers.run( + self.IMAGE, + command=["sleep", "infinity"], # Simple command that definitely works + volumes=volumes, + environment=environment, + working_dir="/", # Start in root, create notebooks dir later + detach=True, + mem_limit=self.memory_limit, + cpu_period=100000, + cpu_quota=int(100000 * self.cpu_limit), + name=f"qc_research_{self.session_id}", + remove=True, # Auto-remove when stopped + labels={ + "mcp.quantconnect.session_id": self.session_id, + "mcp.quantconnect.created_at": self.created_at.isoformat(), + }, + ) + + # Explicitly check if container started + self.container.reload() + if self.container.status != "running": + # If not running, get logs to see what went wrong + logs = self.container.logs().decode() + raise ResearchSessionError(f"Container failed to start (status: {self.container.status}). Logs: {logs}") + + logger.info(f"Container {self.container.id} started successfully with status: {self.container.status}") + + except Exception as e: + logger.error(f"Failed to create/start container: {e}") + raise ResearchSessionError(f"Container creation failed: {e}") + + # Wait a moment for container to start + await asyncio.sleep(2) + + # Initialize Python environment in the container + # First create the notebooks directory if it doesn't exist + mkdir_result = await asyncio.to_thread( + self.container.exec_run, + f"mkdir -p {self.NOTEBOOKS_PATH}", + workdir="/" + ) + + # Test basic Python functionality + init_commands = [ + ("python3 --version", "Check Python version"), + ("python3 -c \"import sys; print('Python initialized:', sys.version)\"", "Test Python import"), + ("python3 -c \"import pandas as pd; import numpy as np; print('Data libraries available')\"", "Test data libraries"), + ] + + for cmd, description in init_commands: + logger.info(f"Running initialization: {description}") + result = await asyncio.to_thread( + self.container.exec_run, + cmd, + workdir="/" # Use root for init commands + ) + if result.exit_code != 0: + error_msg = result.output.decode() if result.output else "No output" + logger.error(f"Init command failed: {cmd} - {error_msg}") + raise ResearchSessionError(f"Container initialization failed ({description}): {error_msg}") + else: + output = result.output.decode() if result.output else "" + logger.info(f"Init success: {output.strip()}") + + self._initialized = True + + # Security logging + security_logger.log_session_created(self.session_id, self.container.id) + logger.info(f"Research session {self.session_id} initialized successfully") + + container_logger = get_container_logger(self.session_id) + container_logger.info(f"Container {self.container.id} ready for session {self.session_id}") + + except Exception as e: + logger.error(f"Failed to initialize research session {self.session_id}: {e}") + await self.close() + raise ResearchSessionError(f"Failed to initialize research session: {e}") + + async def execute( + self, + code: str, + timeout: Optional[int] = None + ) -> Dict[str, Any]: + """ + Execute Python code in the research container with comprehensive error handling. + + Args: + code: Python code to execute + timeout: Execution timeout in seconds (uses default if None) + + Returns: + Dictionary with execution results + """ + if not self._initialized: + await self.initialize() + + if not self.container: + raise ResearchSessionError("Container not available") + + # Security and logging + code_hash = hashlib.sha256(code.encode()).hexdigest()[:16] + container_logger = get_container_logger(self.session_id) + + # Basic security checks + if len(code) > 50000: # 50KB limit + security_logger.log_security_violation( + self.session_id, "CODE_SIZE_LIMIT", f"Code size: {len(code)} bytes" + ) + return { + "status": "error", + "output": "", + "error": "Code size exceeds 50KB limit", + "session_id": self.session_id, + } + + # Check for potentially dangerous operations + dangerous_patterns = [ + "import os", "import subprocess", "import sys", "__import__", + "exec(", "eval(", "compile(", "open(", "file(", + ] + + for pattern in dangerous_patterns: + if pattern in code.lower(): + security_logger.log_security_violation( + self.session_id, "DANGEROUS_CODE_PATTERN", f"Pattern: {pattern}" + ) + container_logger.warning(f"Potentially dangerous code pattern detected: {pattern}") + + self.last_used = datetime.utcnow() + execution_timeout = timeout or self.timeout + + container_logger.info(f"Executing code (hash: {code_hash}, timeout: {execution_timeout}s)") + + try: + # Check container health before execution + try: + container_status = self.container.status + if container_status != "running": + raise ResearchSessionError(f"Container is not running (status: {container_status})") + except Exception as e: + raise ResearchSessionError(f"Failed to check container status: {e}") + + # Execute code directly in container using exec_run (like lean-cli) + # Create a temporary Python script + script_content = f"""#!/usr/bin/env python3 +import sys +import traceback +import pandas as pd +import numpy as np +from io import StringIO + +# Capture stdout +old_stdout = sys.stdout +sys.stdout = captured_output = StringIO() + +try: + # Execute the user code +{chr(10).join(' ' + line for line in code.split(chr(10)))} + + output = captured_output.getvalue() + print(output, file=old_stdout, end='') +except Exception as e: + sys.stdout = old_stdout + print(captured_output.getvalue(), end='') + print(f"Error: {{e}}", file=sys.stderr) + traceback.print_exc() + sys.exit(1) +finally: + sys.stdout = old_stdout +""" + + # Use asyncio timeout for better control + try: + exec_result = await asyncio.wait_for( + asyncio.to_thread( + self.container.exec_run, + f'python3 -c {json.dumps(script_content)}', + workdir=self.NOTEBOOKS_PATH, + stdout=True, + stderr=True, + ), + timeout=execution_timeout + ) + except asyncio.TimeoutError: + security_logger.log_resource_limit_hit( + self.session_id, "EXECUTION_TIMEOUT", f"{execution_timeout}s" + ) + container_logger.error(f"Code execution timed out after {execution_timeout}s") + return { + "status": "error", + "output": "", + "error": f"Code execution timed out after {execution_timeout} seconds", + "session_id": self.session_id, + "timeout": True, + } + + # Removed duplicate exit code check - handled below + + # Parse result with enhanced error handling - simplified approach like lean-cli + output_text = exec_result.output.decode() if exec_result.output else "" + + # Check execution status based on exit code (simpler, more reliable) + if exec_result.exit_code == 0: + # Success + security_logger.log_code_execution(self.session_id, code_hash, True) + container_logger.info(f"Code execution successful (hash: {code_hash})") + + return { + "status": "success", + "output": output_text, + "error": None, + "session_id": self.session_id, + } + else: + # Error + security_logger.log_code_execution(self.session_id, code_hash, False) + container_logger.error(f"Code execution failed (hash: {code_hash}, exit_code: {exec_result.exit_code})") + + return { + "status": "error", + "output": output_text, + "error": f"Code execution failed with exit code {exec_result.exit_code}", + "session_id": self.session_id, + "exit_code": exec_result.exit_code, + } + + except ResearchSessionError: + # Re-raise custom exceptions + raise + except Exception as e: + container_logger.error(f"Unexpected error during code execution: {e}") + security_logger.log_code_execution(self.session_id, code_hash, False) + return { + "status": "error", + "output": "", + "error": f"Unexpected execution error: {str(e)}", + "session_id": self.session_id, + "exception_type": type(e).__name__, + } + + async def save_dataframe( + self, + df: pd.DataFrame, + filename: str, + format: str = "parquet" + ) -> Dict[str, Any]: + """ + Save a pandas DataFrame to the workspace. + + Args: + df: DataFrame to save + filename: Output filename + format: File format (parquet, csv, json) + + Returns: + Operation result + """ + try: + filepath = self.workspace_dir / filename + + if format.lower() == "parquet": + df.to_parquet(filepath) + elif format.lower() == "csv": + df.to_csv(filepath, index=False) + elif format.lower() == "json": + df.to_json(filepath, orient="records", date_format="iso") + else: + raise ValueError(f"Unsupported format: {format}") + + return { + "status": "success", + "message": f"DataFrame saved to {filename}", + "filepath": str(filepath), + "format": format, + "shape": df.shape, + } + + except Exception as e: + return { + "status": "error", + "error": str(e), + "message": f"Failed to save DataFrame to {filename}", + } + + async def load_dataframe( + self, + filename: str, + format: Optional[str] = None + ) -> Dict[str, Any]: + """ + Load a pandas DataFrame from the workspace. + + Args: + filename: Input filename + format: File format (auto-detected if None) + + Returns: + Operation result with DataFrame data + """ + try: + filepath = self.workspace_dir / filename + + if not filepath.exists(): + return { + "status": "error", + "error": f"File {filename} not found in workspace", + } + + # Auto-detect format if not specified + if format is None: + format = filepath.suffix.lower().lstrip(".") + + if format == "parquet": + df = pd.read_parquet(filepath) + elif format == "csv": + df = pd.read_csv(filepath) + elif format == "json": + df = pd.read_json(filepath) + else: + return { + "status": "error", + "error": f"Unsupported format: {format}", + } + + return { + "status": "success", + "message": f"DataFrame loaded from {filename}", + "shape": df.shape, + "columns": df.columns.tolist(), + "dtypes": df.dtypes.to_dict(), + "data": df.to_dict("records")[:100], # Limit to first 100 rows + } + + except Exception as e: + return { + "status": "error", + "error": str(e), + "message": f"Failed to load DataFrame from {filename}", + } + + def is_expired(self, max_idle_time: timedelta = timedelta(hours=1)) -> bool: + """Check if session has been idle too long.""" + return datetime.utcnow() - self.last_used > max_idle_time + + async def close(self, reason: str = "normal") -> None: + """Clean up the research session with enhanced logging.""" + logger.info(f"Closing research session {self.session_id} (reason: {reason})") + container_logger = get_container_logger(self.session_id) + + try: + if self.container: + container_id = self.container.id + try: + container_logger.info(f"Stopping container {container_id}") + self.container.stop(timeout=10) + container_logger.info(f"Container {container_id} stopped successfully") + except Exception as e: + container_logger.warning(f"Error stopping container {container_id}: {e}") + try: + container_logger.info(f"Force killing container {container_id}") + self.container.kill() + container_logger.warning(f"Container {container_id} force killed") + except Exception as e2: + container_logger.error(f"Error killing container {container_id}: {e2}") + + self.container = None + + if self._temp_dir: + container_logger.info(f"Cleaning up temporary directory: {self._temp_dir.name}") + self._temp_dir.cleanup() + self._temp_dir = None + + # Security logging + security_logger.log_session_destroyed(self.session_id, reason) + + except Exception as e: + logger.error(f"Error during session cleanup: {e}") + container_logger.error(f"Cleanup failed: {e}") + + finally: + self._initialized = False + logger.info(f"Research session {self.session_id} cleanup completed") + + def __repr__(self) -> str: + return ( + f"ResearchSession(id={self.session_id}, " + f"initialized={self._initialized}, " + f"created_at={self.created_at.isoformat()})" + ) \ No newline at end of file diff --git a/quantconnect_mcp/src/adapters/session_manager.py b/quantconnect_mcp/src/adapters/session_manager.py new file mode 100644 index 0000000..6d96888 --- /dev/null +++ b/quantconnect_mcp/src/adapters/session_manager.py @@ -0,0 +1,247 @@ +"""Session Manager for QuantConnect Research Sessions""" + +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Dict, List, Optional + +from .research_session import ResearchSession, ResearchSessionError + +logger = logging.getLogger(__name__) + + +class SessionManager: + """ + Manages multiple ResearchSession instances with lifecycle management, + cleanup, and resource monitoring. + """ + + def __init__( + self, + max_sessions: int = 10, + session_timeout: timedelta = timedelta(hours=1), + cleanup_interval: int = 300, # 5 minutes + ): + """ + Initialize the session manager. + + Args: + max_sessions: Maximum number of concurrent sessions + session_timeout: How long idle sessions are kept alive + cleanup_interval: How often to run cleanup in seconds + """ + self.max_sessions = max_sessions + self.session_timeout = session_timeout + self.cleanup_interval = cleanup_interval + + self._sessions: Dict[str, ResearchSession] = {} + self._cleanup_task: Optional[asyncio.Task] = None + self._running = False + + logger.info(f"SessionManager initialized (max_sessions={max_sessions})") + + async def start(self) -> None: + """Start the session manager and cleanup task.""" + if self._running: + return + + self._running = True + self._cleanup_task = asyncio.create_task(self._cleanup_loop()) + logger.info("SessionManager started") + + async def stop(self) -> None: + """Stop the session manager and clean up all sessions.""" + if not self._running: + return + + self._running = False + + # Cancel cleanup task + if self._cleanup_task: + self._cleanup_task.cancel() + try: + await self._cleanup_task + except asyncio.CancelledError: + pass + + # Clean up all sessions + await self.cleanup_all_sessions() + logger.info("SessionManager stopped") + + async def get_or_create_session( + self, + session_id: str, + **session_kwargs + ) -> ResearchSession: + """ + Get an existing session or create a new one. + + Args: + session_id: Unique session identifier + **session_kwargs: Additional arguments for ResearchSession + + Returns: + ResearchSession instance + + Raises: + ResearchSessionError: If max sessions exceeded or creation fails + """ + # Check if session already exists + if session_id in self._sessions: + session = self._sessions[session_id] + session.last_used = datetime.utcnow() + logger.debug(f"Retrieved existing session {session_id}") + return session + + # Check session limit + if len(self._sessions) >= self.max_sessions: + # Try to clean up expired sessions first + await self._cleanup_expired_sessions() + + if len(self._sessions) >= self.max_sessions: + raise ResearchSessionError( + f"Maximum number of sessions ({self.max_sessions}) reached. " + "Please close unused sessions or wait for them to expire." + ) + + # Create new session + try: + session = ResearchSession(session_id=session_id, **session_kwargs) + await session.initialize() + + self._sessions[session_id] = session + logger.info(f"Created new research session {session_id}") + return session + + except Exception as e: + logger.error(f"Failed to create session {session_id}: {e}") + raise ResearchSessionError(f"Failed to create session: {e}") + + async def get_session(self, session_id: str) -> Optional[ResearchSession]: + """ + Get an existing session without creating a new one. + + Args: + session_id: Session identifier + + Returns: + ResearchSession or None if not found + """ + session = self._sessions.get(session_id) + if session: + session.last_used = datetime.utcnow() + return session + + async def close_session(self, session_id: str) -> bool: + """ + Close and remove a specific session. + + Args: + session_id: Session identifier + + Returns: + True if session was found and closed, False otherwise + """ + session = self._sessions.pop(session_id, None) + if session: + await session.close() + logger.info(f"Closed session {session_id}") + return True + return False + + async def cleanup_all_sessions(self) -> None: + """Close and remove all sessions.""" + session_ids = list(self._sessions.keys()) + for session_id in session_ids: + await self.close_session(session_id) + + logger.info(f"Cleaned up {len(session_ids)} sessions") + + def list_sessions(self) -> List[Dict[str, any]]: + """ + Get information about all active sessions. + + Returns: + List of session information dictionaries + """ + return [ + { + "session_id": session.session_id, + "created_at": session.created_at.isoformat(), + "last_used": session.last_used.isoformat(), + "initialized": session._initialized, + "workspace_dir": str(session.workspace_dir), + "memory_limit": session.memory_limit, + "cpu_limit": session.cpu_limit, + } + for session in self._sessions.values() + ] + + def get_session_count(self) -> Dict[str, int]: + """Get session count information.""" + return { + "active_sessions": len(self._sessions), + "max_sessions": self.max_sessions, + "available_slots": max(0, self.max_sessions - len(self._sessions)), + } + + async def _cleanup_expired_sessions(self) -> int: + """Clean up expired sessions and return count of cleaned sessions.""" + expired_sessions = [] + now = datetime.utcnow() + + for session_id, session in self._sessions.items(): + if session.is_expired(self.session_timeout): + expired_sessions.append(session_id) + + # Close expired sessions + for session_id in expired_sessions: + await self.close_session(session_id) + + if expired_sessions: + logger.info(f"Cleaned up {len(expired_sessions)} expired sessions") + + return len(expired_sessions) + + async def _cleanup_loop(self) -> None: + """Background task for periodic session cleanup.""" + logger.info(f"Session cleanup loop started (interval={self.cleanup_interval}s)") + + while self._running: + try: + await asyncio.sleep(self.cleanup_interval) + if self._running: + await self._cleanup_expired_sessions() + except asyncio.CancelledError: + break + except Exception as e: + logger.error(f"Error in cleanup loop: {e}") + + logger.info("Session cleanup loop stopped") + + +# Global session manager instance +_session_manager: Optional[SessionManager] = None + + +def get_session_manager() -> SessionManager: + """Get the global session manager instance.""" + global _session_manager + if _session_manager is None: + _session_manager = SessionManager() + return _session_manager + + +async def initialize_session_manager() -> None: + """Initialize and start the global session manager.""" + manager = get_session_manager() + if not manager._running: + await manager.start() + + +async def shutdown_session_manager() -> None: + """Shutdown the global session manager.""" + global _session_manager + if _session_manager and _session_manager._running: + await _session_manager.stop() + _session_manager = None \ No newline at end of file diff --git a/quantconnect_mcp/src/tools/quantbook_tools.py b/quantconnect_mcp/src/tools/quantbook_tools.py index 827dc42..89693b4 100644 --- a/quantconnect_mcp/src/tools/quantbook_tools.py +++ b/quantconnect_mcp/src/tools/quantbook_tools.py @@ -50,16 +50,30 @@ async def initialize_quantbook( timeout=timeout, ) - # Initialize QuantBook in the container + # Initialize QuantBook in the container (like lean-cli) init_code = """ -from QuantConnect.Research import QuantBook +# Import necessary modules import pandas as pd import numpy as np +import sys +import os -# Create global QuantBook instance -qb = QuantBook() -print(f"QuantBook initialized successfully") -print(f"Available methods: {len([m for m in dir(qb) if not m.startswith('_')]):d}") +# Set up LEAN environment +sys.path.append('/Lean') + +try: + from QuantConnect.Research import QuantBook + from QuantConnect import * + + # Create global QuantBook instance + qb = QuantBook() + print(f"QuantBook initialized successfully in LEAN environment") + print(f"Available methods: {len([m for m in dir(qb) if not m.startswith('_')]):d}") + print(f"LEAN modules loaded: QuantConnect available") +except ImportError as e: + print(f"Warning: LEAN modules not fully available: {e}") + print("Basic Python environment ready (pandas, numpy)") + qb = None """ result = await session.execute(init_code) From a305f127ba3150d16fa4d31e067f3ad849ca23ab Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Thu, 24 Jul 2025 17:47:13 -0400 Subject: [PATCH 04/11] fix package dependencies --- pyproject.toml | 2 +- uv.lock | 13 ++----------- 2 files changed, 3 insertions(+), 12 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7eb9c48..68e5754 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ dependencies = [ "seaborn>=0.13.2", "statsmodels>=0.14.4", "quantconnect-lean", - "quantconnect>=0.1.0", + "docker>=7.1.0", ] [project.optional-dependencies] diff --git a/uv.lock b/uv.lock index 7d2208f..3b522fd 100644 --- a/uv.lock +++ b/uv.lock @@ -1011,15 +1011,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446, upload_time = "2024-08-06T20:33:04.33Z" }, ] -[[package]] -name = "quantconnect" -version = "0.1.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/11/2a/2762a4e3497d35830ac5122fd2bcb7f5fb996f7c8ea47a255c03378466ff/quantconnect-0.1.0.tar.gz", hash = "sha256:9c47411e925141112b40893e0ae1b9364e63b487ce710322cb031d57e022ffd2", size = 921, upload_time = "2020-06-19T21:59:54.104Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/26/30/6624de8b559a496cf34957c4bdb25622713754bcbdfefc06812ed841e92f/quantconnect-0.1.0-py3-none-any.whl", hash = "sha256:c134dcfaf628066932984bc28377c4d717658af108fc70e1f603e5c63856be15", size = 5313, upload_time = "2020-06-19T21:59:52.096Z" }, -] - [[package]] name = "quantconnect-lean" version = "0.1.0" @@ -1035,6 +1026,7 @@ version = "0.1.11" source = { editable = "." } dependencies = [ { name = "arch" }, + { name = "docker" }, { name = "fastmcp" }, { name = "httpx" }, { name = "matplotlib" }, @@ -1042,7 +1034,6 @@ dependencies = [ { name = "pandas" }, { name = "psutil" }, { name = "pytest-asyncio" }, - { name = "quantconnect" }, { name = "quantconnect-lean" }, { name = "scikit-learn" }, { name = "scipy" }, @@ -1069,6 +1060,7 @@ dev = [ [package.metadata] requires-dist = [ { name = "arch", specifier = ">=7.2.0" }, + { name = "docker", specifier = ">=7.1.0" }, { name = "docker", marker = "extra == 'quantbook'", specifier = ">=7.1.0" }, { name = "fastmcp", specifier = ">=2.7.1" }, { name = "httpx", specifier = ">=0.28.1" }, @@ -1077,7 +1069,6 @@ requires-dist = [ { name = "pandas", specifier = ">=2.3.0" }, { name = "psutil", specifier = ">=7.0.0" }, { name = "pytest-asyncio", specifier = ">=1.0.0" }, - { name = "quantconnect", specifier = ">=0.1.0" }, { name = "quantconnect-lean" }, { name = "scikit-learn", specifier = ">=1.7.0" }, { name = "scipy", specifier = ">=1.15.3" }, From e58fbb05cc599f1c8ba68209c1a4777aae640ebe Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Thu, 24 Jul 2025 19:27:31 -0400 Subject: [PATCH 05/11] figuring out container output --- .../src/adapters/research_session.py | 116 ++++-- quantconnect_mcp/src/tools/data_tools.py | 393 +++++++++++------- 2 files changed, 316 insertions(+), 193 deletions(-) diff --git a/quantconnect_mcp/src/adapters/research_session.py b/quantconnect_mcp/src/adapters/research_session.py index 6dc66d3..f7ca159 100644 --- a/quantconnect_mcp/src/adapters/research_session.py +++ b/quantconnect_mcp/src/adapters/research_session.py @@ -253,46 +253,88 @@ async def execute( raise ResearchSessionError(f"Failed to check container status: {e}") # Execute code directly in container using exec_run (like lean-cli) - # Create a temporary Python script + # Create a simple Python script that preserves stdout for Docker capture script_content = f"""#!/usr/bin/env python3 import sys import traceback import pandas as pd import numpy as np -from io import StringIO - -# Capture stdout -old_stdout = sys.stdout -sys.stdout = captured_output = StringIO() try: - # Execute the user code + # Execute the user code directly - let prints go to stdout {chr(10).join(' ' + line for line in code.split(chr(10)))} - - output = captured_output.getvalue() - print(output, file=old_stdout, end='') except Exception as e: - sys.stdout = old_stdout - print(captured_output.getvalue(), end='') + # Print error to stderr so it doesn't interfere with stdout print(f"Error: {{e}}", file=sys.stderr) - traceback.print_exc() + traceback.print_exc(file=sys.stderr) sys.exit(1) -finally: - sys.stdout = old_stdout """ - # Use asyncio timeout for better control + # Create execution command + exec_cmd = f'python3 -c {json.dumps(script_content)}' + + # Use Docker streaming API for real-time output capture (like LEAN CLI) try: - exec_result = await asyncio.wait_for( - asyncio.to_thread( - self.container.exec_run, - f'python3 -c {json.dumps(script_content)}', - workdir=self.NOTEBOOKS_PATH, - stdout=True, - stderr=True, - ), - timeout=execution_timeout + # Create exec instance + exec_instance = await asyncio.to_thread( + self.container.client.api.exec_create, + self.container.id, + exec_cmd, + stdout=True, + stderr=True, + workdir=self.NOTEBOOKS_PATH, + tty=False + ) + + # Start execution and get output stream + output_stream = await asyncio.to_thread( + self.container.client.api.exec_start, + exec_instance['Id'], + stream=True, + tty=False + ) + + # Collect output with timeout + output_chunks = [] + start_time = asyncio.get_event_loop().time() + + async def collect_output(): + chunk_buffer = b"" + for chunk in output_stream: + # Check timeout + if asyncio.get_event_loop().time() - start_time > execution_timeout: + raise asyncio.TimeoutError() + + # Buffer management for complete lines (like LEAN CLI) + chunk_buffer += chunk + + # Process complete lines + while b'\n' in chunk_buffer: + line_end = chunk_buffer.find(b'\n') + line = chunk_buffer[:line_end + 1] + chunk_buffer = chunk_buffer[line_end + 1:] + output_chunks.append(line) + + # Yield control to allow timeout checks + await asyncio.sleep(0) + + # Don't forget remaining buffer content + if chunk_buffer: + output_chunks.append(chunk_buffer) + + # Run output collection with timeout + await asyncio.wait_for(collect_output(), timeout=execution_timeout) + + # Get execution result + exec_info = await asyncio.to_thread( + self.container.client.api.exec_inspect, + exec_instance['Id'] ) + exit_code = exec_info.get('ExitCode', 0) + + # Decode collected output + full_output = b''.join(output_chunks).decode('utf-8', errors='replace') + except asyncio.TimeoutError: security_logger.log_resource_limit_hit( self.session_id, "EXECUTION_TIMEOUT", f"{execution_timeout}s" @@ -306,34 +348,32 @@ async def execute( "timeout": True, } - # Removed duplicate exit code check - handled below - - # Parse result with enhanced error handling - simplified approach like lean-cli - output_text = exec_result.output.decode() if exec_result.output else "" + # Log the output for debugging + container_logger.debug(f"Container output (exit_code: {exit_code}): {repr(full_output[:200])}") - # Check execution status based on exit code (simpler, more reliable) - if exec_result.exit_code == 0: - # Success + # Check execution status based on exit code + if exit_code == 0: + # Success - return stdout content security_logger.log_code_execution(self.session_id, code_hash, True) container_logger.info(f"Code execution successful (hash: {code_hash})") return { "status": "success", - "output": output_text, + "output": full_output.strip(), # Remove trailing whitespace "error": None, "session_id": self.session_id, } else: - # Error + # Error - output contains both stdout and stderr security_logger.log_code_execution(self.session_id, code_hash, False) - container_logger.error(f"Code execution failed (hash: {code_hash}, exit_code: {exec_result.exit_code})") + container_logger.error(f"Code execution failed (hash: {code_hash}, exit_code: {exit_code})") return { "status": "error", - "output": output_text, - "error": f"Code execution failed with exit code {exec_result.exit_code}", + "output": full_output.strip(), + "error": f"Code execution failed with exit code {exit_code}", "session_id": self.session_id, - "exit_code": exec_result.exit_code, + "exit_code": exit_code, } except ResearchSessionError: diff --git a/quantconnect_mcp/src/tools/data_tools.py b/quantconnect_mcp/src/tools/data_tools.py index 2cf273b..26c2c19 100644 --- a/quantconnect_mcp/src/tools/data_tools.py +++ b/quantconnect_mcp/src/tools/data_tools.py @@ -73,6 +73,12 @@ async def add_equity( "success": True }} + # Print result as JSON for MCP to parse + import json + print("=== QUANTBOOK_RESULT_START ===") + print(json.dumps(result)) + print("=== QUANTBOOK_RESULT_END ===") + except Exception as e: print(f"Failed to add equity '{ticker}': {{e}}") result = {{ @@ -80,6 +86,12 @@ async def add_equity( "error": str(e), "success": False }} + + # Print error result as JSON + import json + print("=== QUANTBOOK_RESULT_START ===") + print(json.dumps(result)) + print("=== QUANTBOOK_RESULT_END ===") """ execution_result = await session.execute(add_equity_code) @@ -92,14 +104,59 @@ async def add_equity( "execution_output": execution_result.get("output", ""), } - return { - "status": "success", - "ticker": ticker, - "resolution": resolution, - "message": f"Successfully added equity '{ticker}' with {resolution} resolution", - "execution_output": execution_result.get("output", ""), - "instance_name": instance_name, - } + # Parse the JSON result from container output + output = execution_result.get("output", "") + parsed_result = None + + try: + # Extract JSON result from container output + if "=== QUANTBOOK_RESULT_START ===" in output and "=== QUANTBOOK_RESULT_END ===" in output: + start_marker = output.find("=== QUANTBOOK_RESULT_START ===") + end_marker = output.find("=== QUANTBOOK_RESULT_END ===") + if start_marker != -1 and end_marker != -1: + json_start = start_marker + len("=== QUANTBOOK_RESULT_START ===\n") + json_content = output[json_start:end_marker].strip() + parsed_result = json.loads(json_content) + + if parsed_result and parsed_result.get("success"): + # Return successful result with parsed data + return { + "status": "success", + "ticker": ticker, + "symbol": parsed_result.get("symbol", ticker), + "resolution": resolution, + "message": f"Successfully added equity '{ticker}' with {resolution} resolution", + "execution_output": output, + "instance_name": instance_name, + } + elif parsed_result and not parsed_result.get("success"): + # Container execution succeeded but equity addition failed + return { + "status": "error", + "error": parsed_result.get("error", "Unknown equity addition error"), + "message": f"Failed to add equity '{ticker}'", + "execution_output": output, + "instance_name": instance_name, + } + else: + # Fallback if JSON parsing fails but execution succeeded + return { + "status": "success", + "ticker": ticker, + "resolution": resolution, + "message": f"Successfully added equity '{ticker}' with {resolution} resolution", + "execution_output": output, + "instance_name": instance_name, + } + + except json.JSONDecodeError as e: + return { + "status": "error", + "error": f"Failed to parse container result: {e}", + "message": f"Container executed but result parsing failed", + "execution_output": output, + "instance_name": instance_name, + } except Exception as e: logger.error(f"Failed to add equity '{ticker}' in instance '{instance_name}': {e}") @@ -231,86 +288,180 @@ async def get_history( Returns: Dictionary containing historical data """ - qb = get_quantbook_instance(instance_name) - if qb is None: + session = await get_quantbook_session(instance_name) + if session is None: return { "status": "error", "error": f"QuantBook instance '{instance_name}' not found", + "message": "Initialize a QuantBook instance first using initialize_quantbook", } try: - from QuantConnect import Resolution # type: ignore - from datetime import datetime - - # Parse dates - start = datetime.strptime(start_date, "%Y-%m-%d") - end = datetime.strptime(end_date, "%Y-%m-%d") - - # Map resolution - resolution_map = { - "Minute": Resolution.Minute, - "Hour": Resolution.Hour, - "Daily": Resolution.Daily, - } - - if resolution not in resolution_map: + # Validate resolution + valid_resolutions = ["Minute", "Hour", "Daily"] + if resolution not in valid_resolutions: return { "status": "error", - "error": f"Invalid resolution '{resolution}'. Must be one of: {list(resolution_map.keys())}", + "error": f"Invalid resolution '{resolution}'. Must be one of: {valid_resolutions}", } # Handle single symbol vs multiple symbols if isinstance(symbols, str): - symbols = [symbols] - - # Get securities keys for the symbols - security_keys = [] - for symbol in symbols: - # Find the security in qb.Securities - found = False - for sec_key in qb.Securities.Keys: - if str(sec_key).upper() == symbol.upper(): - security_keys.append(sec_key) - found = True - break - if not found: - return { - "status": "error", - "error": f"Symbol '{symbol}' not found in securities. Add it first using add_equity.", - } + symbols_list = [symbols] + else: + symbols_list = symbols - # Get historical data - history = qb.History(security_keys, start, end, resolution_map[resolution]) + # Convert symbols list to Python code representation + symbols_str = str(symbols_list) + + # Build fields filter if specified + fields_filter = "" + if fields: + fields_str = str(fields) + fields_filter = f""" + # Filter specific fields if requested + if not history.empty: + available_fields = [col for col in history.columns if col in {fields_str}] + if available_fields: + history = history[available_fields] +""" - if history.empty: - return { - "status": "success", - "data": {}, - "message": "No data found for the specified period", - } + # Execute code to get historical data in container + get_history_code = f""" +from QuantConnect import Resolution +from datetime import datetime +import pandas as pd - # Convert to dictionary format - if fields: - # Filter specific fields - available_fields = [col for col in history.columns if col in fields] - if available_fields: - history = history[available_fields] - - # Convert to JSON-serializable format - data = {} - for col in history.columns: - if col in ["open", "high", "low", "close", "volume"]: +# Map string resolution to enum +resolution_map = {{ + "Minute": Resolution.Minute, + "Hour": Resolution.Hour, + "Daily": Resolution.Daily, +}} + +try: + # Parse dates + start_date = datetime.strptime("{start_date}", "%Y-%m-%d") + end_date = datetime.strptime("{end_date}", "%Y-%m-%d") + + symbols_list = {symbols_str} + resolution_val = resolution_map["{resolution}"] + + # Get historical data + history = qb.History(symbols_list, start_date, end_date, resolution_val) + + print(f"Retrieved history for {{symbols_list}}: {{len(history)}} data points") + + if history.empty: + print("No data found for the specified period") + result = {{ + "status": "success", + "data": {{}}, + "message": "No data found for the specified period", + "symbols": symbols_list, + "start_date": "{start_date}", + "end_date": "{end_date}", + "resolution": "{resolution}", + "shape": [0, 0] + }} + else: + {fields_filter} + + # Convert to JSON-serializable format + data = {{}} + for col in history.columns: + if col in ["open", "high", "low", "close", "volume"]: + if len(symbols_list) == 1: + # Single symbol - simpler format + data[col] = history[col].to_dict() + else: + # Multiple symbols - unstack format data[col] = history[col].unstack(level=0).to_dict() + + result = {{ + "status": "success", + "symbols": symbols_list, + "start_date": "{start_date}", + "end_date": "{end_date}", + "resolution": "{resolution}", + "data": data, + "shape": list(history.shape), + }} + + # Print result as JSON for MCP to parse + import json + print("=== QUANTBOOK_RESULT_START ===") + print(json.dumps(result, default=str)) # default=str handles datetime objects + print("=== QUANTBOOK_RESULT_END ===") + + print("Historical data retrieval completed successfully") + +except Exception as e: + print(f"Error retrieving historical data: {{e}}") + result = {{ + "status": "error", + "error": str(e), + "message": f"Failed to retrieve history for symbols: {symbols_str}", + }} + + # Print error result as JSON + import json + print("=== QUANTBOOK_RESULT_START ===") + print(json.dumps(result)) + print("=== QUANTBOOK_RESULT_END ===") +""" - return { - "status": "success", - "symbols": symbols, - "start_date": start_date, - "end_date": end_date, - "resolution": resolution, - "data": data, - "shape": list(history.shape), - } + execution_result = await session.execute(get_history_code) + + if execution_result["status"] != "success": + return { + "status": "error", + "error": execution_result.get("error", "Unknown error"), + "message": f"Failed to retrieve history for symbols: {symbols}", + "execution_output": execution_result.get("output", ""), + } + + # Parse the JSON result from container output + output = execution_result.get("output", "") + parsed_result = None + + try: + # Extract JSON result from container output + if "=== QUANTBOOK_RESULT_START ===" in output and "=== QUANTBOOK_RESULT_END ===" in output: + start_marker = output.find("=== QUANTBOOK_RESULT_START ===") + end_marker = output.find("=== QUANTBOOK_RESULT_END ===") + if start_marker != -1 and end_marker != -1: + json_start = start_marker + len("=== QUANTBOOK_RESULT_START ===\n") + json_content = output[json_start:end_marker].strip() + parsed_result = json.loads(json_content) + + if parsed_result: + # Return the parsed result with additional metadata + result = parsed_result.copy() + result["execution_output"] = output + result["instance_name"] = instance_name + return result + else: + # Fallback if JSON parsing fails + return { + "status": "success", + "symbols": symbols, + "start_date": start_date, + "end_date": end_date, + "resolution": resolution, + "message": f"Successfully executed but no structured result found", + "execution_output": output, + "instance_name": instance_name, + } + + except json.JSONDecodeError as e: + return { + "status": "error", + "error": f"Failed to parse container result: {e}", + "message": f"Container executed but result parsing failed", + "execution_output": output, + "instance_name": instance_name, + } except Exception as e: return { @@ -334,45 +485,21 @@ async def add_alternative_data( Returns: Dictionary containing alternative data subscription info """ - qb = get_quantbook_instance(instance_name) - if qb is None: + session = await get_quantbook_session(instance_name) + if session is None: return { "status": "error", "error": f"QuantBook instance '{instance_name}' not found", + "message": "Initialize a QuantBook instance first using initialize_quantbook", } try: - # Map data types to QuantConnect classes - if data_type == "SmartInsiderTransaction": - from QuantConnect.DataSource import SmartInsiderTransaction # type: ignore - - # Find the symbol in securities - target_symbol = None - for sec_key in qb.Securities.Keys: - if str(sec_key).upper() == symbol.upper(): - target_symbol = sec_key - break - - if target_symbol is None: - return { - "status": "error", - "error": f"Symbol '{symbol}' not found. Add it as equity first.", - } - - alt_symbol = qb.AddData(SmartInsiderTransaction, target_symbol).Symbol - - return { - "status": "success", - "data_type": data_type, - "symbol": symbol, - "alt_symbol": str(alt_symbol), - "message": f"Successfully added {data_type} data for {symbol}", - } - else: - return { - "status": "error", - "error": f"Unsupported data type '{data_type}'. Currently supported: SmartInsiderTransaction", - } + # TODO: Convert to container execution like other functions + return { + "status": "error", + "error": "Alternative data functions need to be updated for container execution", + "message": f"add_alternative_data is temporarily disabled pending container execution update", + } except Exception as e: return { @@ -402,64 +529,20 @@ async def get_alternative_data_history( Returns: Dictionary containing alternative data history """ - qb = get_quantbook_instance(instance_name) - if qb is None: + session = await get_quantbook_session(instance_name) + if session is None: return { "status": "error", "error": f"QuantBook instance '{instance_name}' not found", + "message": "Initialize a QuantBook instance first using initialize_quantbook", } try: - from datetime import datetime - - start = datetime.strptime(start_date, "%Y-%m-%d") - end = datetime.strptime(end_date, "%Y-%m-%d") - - if isinstance(symbols, str): - symbols = [symbols] - - # Get alternative data symbols - alt_symbols = [] - for symbol in symbols: - # Find alternative data symbols for this equity - for sec_key in qb.Securities.Keys: - if ( - data_type.lower() in str(sec_key).lower() - and symbol.upper() in str(sec_key).upper() - ): - alt_symbols.append(sec_key) - - if not alt_symbols: - return { - "status": "error", - "error": f"No {data_type} data found for symbols {symbols}. Add alternative data first.", - } - - # Get history - from QuantConnect import Resolution # type: ignore - - history = qb.History(alt_symbols, start, end, Resolution.Daily) - - if history.empty: - return { - "status": "success", - "data": {}, - "message": "No alternative data found for the specified period", - } - - # Convert to JSON format - data = {} - for col in history.columns: - data[col] = history[col].unstack(level=0).to_dict() - + # TODO: Convert to container execution like other functions return { - "status": "success", - "data_type": data_type, - "symbols": symbols, - "start_date": start_date, - "end_date": end_date, - "data": data, - "shape": list(history.shape), + "status": "error", + "error": "Alternative data functions need to be updated for container execution", + "message": f"get_alternative_data_history is temporarily disabled pending container execution update", } except Exception as e: From aa979bc814b67d82a3e251f452f501999e13d839 Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Fri, 25 Jul 2025 13:18:01 -0400 Subject: [PATCH 06/11] working docker, needs full config and data sync --- .../src/adapters/research_session.py | 364 ++++++++++++++---- 1 file changed, 296 insertions(+), 68 deletions(-) diff --git a/quantconnect_mcp/src/adapters/research_session.py b/quantconnect_mcp/src/adapters/research_session.py index f7ca159..b276c01 100644 --- a/quantconnect_mcp/src/adapters/research_session.py +++ b/quantconnect_mcp/src/adapters/research_session.py @@ -4,6 +4,7 @@ import hashlib import json import logging +import os import tempfile import uuid from datetime import datetime, timedelta @@ -14,6 +15,7 @@ import docker.types import pandas as pd from docker.models.containers import Container +from docker.types import Mount from .logging_config import get_container_logger, security_logger @@ -33,9 +35,10 @@ class ResearchSession: and provides methods to execute code and exchange data. """ - IMAGE = "quantconnect/research:latest" # Use DEFAULT_RESEARCH_IMAGE from lean constants + IMAGE = "quantconnect/research:latest" # Use research image as intended CONTAINER_WORKSPACE = "/Lean" # Match LEAN_ROOT_PATH NOTEBOOKS_PATH = "/Lean/Notebooks" + DATA_PATH = "/Lean/Data" TIMEOUT_DEFAULT = 300 # 5 minutes def __init__( @@ -45,6 +48,7 @@ def __init__( memory_limit: str = "2g", cpu_limit: float = 1.0, timeout: int = TIMEOUT_DEFAULT, + port: Optional[int] = None, ): """ Initialize a new research session. @@ -55,6 +59,7 @@ def __init__( memory_limit: Container memory limit (e.g., "2g", "512m") cpu_limit: Container CPU limit (fraction of CPU) timeout: Default execution timeout in seconds + port: Local port to expose Jupyter Lab on (default: env var QUANTBOOK_DOCKER_PORT or 8888) """ self.session_id = session_id or f"qb_{uuid.uuid4().hex[:8]}" self.memory_limit = memory_limit @@ -63,6 +68,13 @@ def __init__( self.created_at = datetime.utcnow() self.last_used = self.created_at + # Get port from parameter, env var, or default + import os + if port is not None: + self.port = port + else: + self.port = int(os.environ.get("QUANTBOOK_DOCKER_PORT", "8888")) + # Setup workspace if workspace_dir: self.workspace_dir = Path(workspace_dir) @@ -72,12 +84,24 @@ def __init__( self._temp_dir = tempfile.TemporaryDirectory(prefix=f"qc_research_{self.session_id}_") self.workspace_dir = Path(self._temp_dir.name) + # Create necessary directories + self.notebooks_dir = self.workspace_dir / "Notebooks" + self.notebooks_dir.mkdir(parents=True, exist_ok=True) + + # Create data directory structure (minimal for research) + self.data_dir = self.workspace_dir / "Data" + self.data_dir.mkdir(parents=True, exist_ok=True) + + # Create temp directory for configs + self.temp_config_dir = self.workspace_dir / "temp" + self.temp_config_dir.mkdir(parents=True, exist_ok=True) + # Docker client and container self.client = docker.from_env() self.container: Optional[Container] = None self._initialized = False - logger.info(f"Created research session {self.session_id}") + logger.info(f"Created research session {self.session_id} (port: {self.port})") async def initialize(self) -> None: """Initialize the Docker container.""" @@ -92,14 +116,85 @@ async def initialize(self) -> None: logger.info(f"Pulling image {self.IMAGE}...") self.client.images.pull(self.IMAGE) - # Start container with proper LEAN research environment - # Use similar approach to lean-cli research command - volumes = { - str(self.workspace_dir): { - "bind": self.NOTEBOOKS_PATH, - "mode": "rw" + # Create the Lean config file from template + template_path = Path(__file__).parent / "lean_config_template.json" + with open(template_path, "r") as f: + lean_config = json.load(f) + + # Update config with research-specific settings + lean_config["research-object-store-name"] = self.session_id + lean_config["job-organization-id"] = os.environ.get("QUANTCONNECT_ORGANIZATION_ID", "0") + lean_config["job-user-id"] = os.environ.get("QUANTCONNECT_USER_ID", "0") + lean_config["api-access-token"] = os.environ.get("QUANTCONNECT_API_TOKEN", "") + + # Save config to temp directory + config_path = self.temp_config_dir / "config.json" + with open(config_path, "w") as f: + json.dump(lean_config, f, indent=2) + + # Create a default research notebook if none exists + default_notebook = self.notebooks_dir / "research.ipynb" + if not default_notebook.exists(): + notebook_content = { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": ["# QuantConnect Research Environment\n", + "Welcome to the QuantConnect Research Environment. ", + "Here you can perform historical research using the QuantBook API."] + }, + { + "cell_type": "code", + "metadata": {}, + "source": ["# QuantBook is automatically available as 'qb'\n", + "# Documentation: https://www.quantconnect.com/docs/v2/research-environment"] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } - } + with open(default_notebook, "w") as f: + json.dump(notebook_content, f, indent=2) + + # Set up mounts exactly like LEAN CLI + mounts = [ + # Mount notebooks directory + Mount( + target=self.NOTEBOOKS_PATH, + source=str(self.notebooks_dir), + type="bind", + read_only=False + ), + # Mount data directory (even if minimal) + Mount( + target=self.DATA_PATH, + source=str(self.data_dir), + type="bind", + read_only=True + ), + # Mount config in root + Mount( + target="/Lean/config.json", + source=str(config_path), + type="bind", + read_only=True + ), + # Also mount config in notebooks directory (like LEAN CLI) + Mount( + target=f"{self.NOTEBOOKS_PATH}/config.json", + source=str(config_path), + type="bind", + read_only=True + ) + ] # Add environment variables like lean-cli does environment = { @@ -108,14 +203,58 @@ async def initialize(self) -> None: "PYTHONPATH": "/Lean" } - # Create container with simplified configuration to ensure it starts + # Create the startup script similar to LEAN CLI + shell_script_commands = [ + "#!/usr/bin/env bash", + "set -e", + # Setup Jupyter config + "mkdir -p ~/.jupyter", + 'echo "c.ServerApp.disable_check_xsrf = True\nc.ServerApp.tornado_settings = {\'headers\': {\'Content-Security-Policy\': \'frame-ancestors self *\'}}" > ~/.jupyter/jupyter_server_config.py', + "mkdir -p ~/.ipython/profile_default/static/custom", + 'echo "#header-container { display: none !important; }" > ~/.ipython/profile_default/static/custom/custom.css', + # Start the research environment (look for start.sh or similar) + "if [ -f /start.sh ]; then", + " echo 'Starting research environment with /start.sh'", + " exec /start.sh", + "elif [ -f /opt/miniconda3/bin/jupyter ]; then", + " echo 'Starting Jupyter Lab directly'", + " cd /Lean/Notebooks", + " exec jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.allow_origin='*'", + "else", + " echo 'No Jupyter found, keeping container alive'", + " exec sleep infinity", + "fi" + ] + + # Write the startup script to a temporary file + if self._temp_dir: + startup_script_path = Path(self._temp_dir.name) / "lean-cli-start.sh" + else: + startup_script_path = self.workspace_dir / "lean-cli-start.sh" + + startup_script_path.parent.mkdir(parents=True, exist_ok=True) + with open(startup_script_path, "w", encoding="utf-8", newline="\n") as file: + file.write("\n".join(shell_script_commands) + "\n") + + # Make the script executable + os.chmod(startup_script_path, 0o755) + + # Add the startup script mount + mounts.append(Mount( + target="/lean-cli-start.sh", + source=str(startup_script_path), + type="bind", + read_only=True + )) + + # Create container with the startup script as entrypoint try: self.container = self.client.containers.run( self.IMAGE, - command=["sleep", "infinity"], # Simple command that definitely works - volumes=volumes, + entrypoint=["bash", "/lean-cli-start.sh"], + mounts=mounts, environment=environment, - working_dir="/", # Start in root, create notebooks dir later + working_dir=self.NOTEBOOKS_PATH, detach=True, mem_limit=self.memory_limit, cpu_period=100000, @@ -126,6 +265,7 @@ async def initialize(self) -> None: "mcp.quantconnect.session_id": self.session_id, "mcp.quantconnect.created_at": self.created_at.isoformat(), }, + ports={"8888/tcp": str(self.port)}, # Expose Jupyter port to local port ) # Explicitly check if container started @@ -141,8 +281,32 @@ async def initialize(self) -> None: logger.error(f"Failed to create/start container: {e}") raise ResearchSessionError(f"Container creation failed: {e}") - # Wait a moment for container to start - await asyncio.sleep(2) + # Wait for Jupyter to start up + logger.info("Waiting for Jupyter kernel to initialize...") + jupyter_ready = False + for i in range(12): # Check for up to 60 seconds + await asyncio.sleep(5) + + # Check if Jupyter is running + jupyter_check = await asyncio.to_thread( + self.container.exec_run, + "ps aux | grep -E 'jupyter-lab|jupyter-notebook' | grep -v grep", + workdir="/" + ) + + if jupyter_check.exit_code == 0 and jupyter_check.output: + logger.info("Jupyter is running") + jupyter_ready = True + break + else: + logger.info(f"Waiting for Jupyter... ({i+1}/12)") + + if jupyter_ready: + logger.info(f"Jupyter kernel is ready on port {self.port}") + # Give it a bit more time to fully initialize the kernel + await asyncio.sleep(5) + else: + logger.warning("Jupyter did not start within timeout, proceeding anyway") # Initialize Python environment in the container # First create the notebooks directory if it doesn't exist @@ -157,19 +321,25 @@ async def initialize(self) -> None: ("python3 --version", "Check Python version"), ("python3 -c \"import sys; print('Python initialized:', sys.version)\"", "Test Python import"), ("python3 -c \"import pandas as pd; import numpy as np; print('Data libraries available')\"", "Test data libraries"), + ("ls -la /Lean/", "Check LEAN directory"), + (["/bin/bash", "-c", "ls -la /opt/miniconda3/share/jupyter/kernels/ 2>/dev/null || echo 'No Jupyter kernels directory'"], "Check Jupyter kernels"), ] for cmd, description in init_commands: logger.info(f"Running initialization: {description}") result = await asyncio.to_thread( self.container.exec_run, - cmd, + cmd if isinstance(cmd, list) else cmd, workdir="/" # Use root for init commands ) if result.exit_code != 0: - error_msg = result.output.decode() if result.output else "No output" - logger.error(f"Init command failed: {cmd} - {error_msg}") - raise ResearchSessionError(f"Container initialization failed ({description}): {error_msg}") + # Don't fail on non-critical checks + if "Check Jupyter kernels" in description: + logger.warning(f"Non-critical check failed: {description}") + else: + error_msg = result.output.decode() if result.output else "No output" + logger.error(f"Init command failed: {cmd} - {error_msg}") + raise ResearchSessionError(f"Container initialization failed ({description}): {error_msg}") else: output = result.output.decode() if result.output else "" logger.info(f"Init success: {output.strip()}") @@ -253,93 +423,151 @@ async def execute( raise ResearchSessionError(f"Failed to check container status: {e}") # Execute code directly in container using exec_run (like lean-cli) - # Create a simple Python script that preserves stdout for Docker capture + # Create a Python script that includes QuantBook initialization script_content = f"""#!/usr/bin/env python3 import sys import traceback import pandas as pd import numpy as np +# Import datetime first +from datetime import datetime, timedelta + +# In QuantConnect Research environment, qb should be pre-initialized by the kernel +# The user's code will have access to qb and other QuantConnect objects + try: - # Execute the user code directly - let prints go to stdout + # Execute the user code with qb available {chr(10).join(' ' + line for line in code.split(chr(10)))} except Exception as e: # Print error to stderr so it doesn't interfere with stdout - print(f"Error: {{e}}", file=sys.stderr) + print(f"Error: {{e}}", file=sys.stderr, flush=True) traceback.print_exc(file=sys.stderr) sys.exit(1) """ - # Create execution command - exec_cmd = f'python3 -c {json.dumps(script_content)}' + # Debug logging + import os + from datetime import datetime as dt + debug_log_path = "/Users/taylorwilsdon/git/quantconnect-mcp/mcp_debug_output.log" + with open(debug_log_path, "a") as debug_file: + debug_file.write(f"\n=== EXECUTION DEBUG {dt.now().isoformat()} ===\n") + debug_file.write(f"Session: {self.session_id}\n") + debug_file.write(f"Code hash: {code_hash}\n") + debug_file.write(f"Script preview: {script_content[:200]}...\n") + + # Test with the low-level API to see if we get output + test_exec = await asyncio.to_thread( + self.container.client.api.exec_create, + self.container.id, + 'echo "Low-level API test"', + stdout=True, + stderr=True + ) + test_output = await asyncio.to_thread( + self.container.client.api.exec_start, + test_exec['Id'], + stream=False + ) + + with open(debug_log_path, "a") as debug_file: + debug_file.write(f"Low-level test output: {test_output}\n") - # Use Docker streaming API for real-time output capture (like LEAN CLI) + # Use low-level Docker API with file-based execution try: - # Create exec instance - exec_instance = await asyncio.to_thread( + # First, write the script to a file in the container + script_filename = f"quantbook_exec_{code_hash}.py" + script_path = f"{self.NOTEBOOKS_PATH}/{script_filename}" + + # Write script content to file + write_cmd = f"cat > {script_path} << 'EOF'\n{script_content}\nEOF" + write_exec = await asyncio.to_thread( self.container.client.api.exec_create, self.container.id, - exec_cmd, + ['/bin/sh', '-c', write_cmd], stdout=True, stderr=True, - workdir=self.NOTEBOOKS_PATH, - tty=False + workdir=self.NOTEBOOKS_PATH ) - - # Start execution and get output stream - output_stream = await asyncio.to_thread( + write_result = await asyncio.to_thread( self.container.client.api.exec_start, - exec_instance['Id'], - stream=True, - tty=False + write_exec['Id'], + stream=False ) - # Collect output with timeout - output_chunks = [] - start_time = asyncio.get_event_loop().time() + # Debug log the write result + with open(debug_log_path, "a") as debug_file: + debug_file.write(f"Script write result: {write_result}\n") - async def collect_output(): - chunk_buffer = b"" - for chunk in output_stream: - # Check timeout - if asyncio.get_event_loop().time() - start_time > execution_timeout: - raise asyncio.TimeoutError() - - # Buffer management for complete lines (like LEAN CLI) - chunk_buffer += chunk - - # Process complete lines - while b'\n' in chunk_buffer: - line_end = chunk_buffer.find(b'\n') - line = chunk_buffer[:line_end + 1] - chunk_buffer = chunk_buffer[line_end + 1:] - output_chunks.append(line) - - # Yield control to allow timeout checks - await asyncio.sleep(0) - - # Don't forget remaining buffer content - if chunk_buffer: - output_chunks.append(chunk_buffer) + # Now execute the script file + exec_cmd = f'python3 {script_filename}' + exec_instance = await asyncio.to_thread( + self.container.client.api.exec_create, + self.container.id, + exec_cmd, + stdout=True, + stderr=True, + workdir=self.NOTEBOOKS_PATH + ) - # Run output collection with timeout - await asyncio.wait_for(collect_output(), timeout=execution_timeout) + # Start execution and get output (not streaming) + exec_output = await asyncio.wait_for( + asyncio.to_thread( + self.container.client.api.exec_start, + exec_instance['Id'], + stream=False + ), + timeout=execution_timeout + ) - # Get execution result + # Get exec info for exit code exec_info = await asyncio.to_thread( self.container.client.api.exec_inspect, exec_instance['Id'] ) - exit_code = exec_info.get('ExitCode', 0) - # Decode collected output - full_output = b''.join(output_chunks).decode('utf-8', errors='replace') + exit_code = exec_info.get('ExitCode', -1) + + # Process the output + stdout_output = exec_output.decode('utf-8', errors='replace') if exec_output else "" + stderr_output = "" + + # Debug log the raw output + with open(debug_log_path, "a") as debug_file: + debug_file.write(f"Low-level exec_output type: {type(exec_output)}\n") + debug_file.write(f"Low-level exec_output length: {len(exec_output) if exec_output else 0}\n") + debug_file.write(f"Low-level exec_output preview: {repr(exec_output[:500]) if exec_output else 'None'}\n") + debug_file.write(f"Exit code: {exit_code}\n") + debug_file.write(f"Stdout length: {len(stdout_output)}\n") + debug_file.write(f"Stdout preview: {repr(stdout_output[:500])}\n") + + # Clean up the script file + cleanup_exec = await asyncio.to_thread( + self.container.client.api.exec_create, + self.container.id, + f'rm -f {script_filename}', + workdir=self.NOTEBOOKS_PATH + ) + await asyncio.to_thread( + self.container.client.api.exec_start, + cleanup_exec['Id'], + stream=False + ) + + # Combine outputs for return + full_output = stdout_output + if stderr_output and exit_code != 0: + full_output = stdout_output + "\n[STDERR]\n" + stderr_output except asyncio.TimeoutError: security_logger.log_resource_limit_hit( self.session_id, "EXECUTION_TIMEOUT", f"{execution_timeout}s" ) container_logger.error(f"Code execution timed out after {execution_timeout}s") + + with open(debug_log_path, "a") as debug_file: + debug_file.write(f"TIMEOUT after {execution_timeout}s\n") + return { "status": "error", "output": "", From 7d84884d2c9cf949ab589a5d12a6038d96995c42 Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Fri, 25 Jul 2025 15:28:38 -0400 Subject: [PATCH 07/11] always use jupyter research notebook for execution --- quantconnect_mcp/src/adapters/__init__.py | 2 +- .../src/adapters/jupyter_kernel_client.py | 118 +++ .../src/adapters/research_session_jupyter.py | 337 +++++++++ .../src/adapters/research_session_lean_cli.py | 679 ++++++++++++++++++ ...session.py => research_session_old.py.bak} | 140 +++- .../src/adapters/session_manager.py | 7 +- quantconnect_mcp/src/tools/data_tools.py | 194 ++--- quantconnect_mcp/src/tools/quantbook_tools.py | 295 ++++++-- 8 files changed, 1583 insertions(+), 189 deletions(-) create mode 100644 quantconnect_mcp/src/adapters/jupyter_kernel_client.py create mode 100644 quantconnect_mcp/src/adapters/research_session_jupyter.py create mode 100644 quantconnect_mcp/src/adapters/research_session_lean_cli.py rename quantconnect_mcp/src/adapters/{research_session.py => research_session_old.py.bak} (83%) diff --git a/quantconnect_mcp/src/adapters/__init__.py b/quantconnect_mcp/src/adapters/__init__.py index 0a49d65..88534ad 100644 --- a/quantconnect_mcp/src/adapters/__init__.py +++ b/quantconnect_mcp/src/adapters/__init__.py @@ -1,6 +1,6 @@ """Adapter modules for external integrations.""" -from .research_session import ResearchSession +from .research_session_lean_cli import ResearchSession from .session_manager import SessionManager, get_session_manager, initialize_session_manager from .logging_config import setup_logging, security_logger diff --git a/quantconnect_mcp/src/adapters/jupyter_kernel_client.py b/quantconnect_mcp/src/adapters/jupyter_kernel_client.py new file mode 100644 index 0000000..98e47d1 --- /dev/null +++ b/quantconnect_mcp/src/adapters/jupyter_kernel_client.py @@ -0,0 +1,118 @@ +"""Jupyter Kernel Client for executing code in research containers.""" + +import asyncio +import json +import logging +import uuid +from typing import Any, Dict, Optional + +import httpx + +logger = logging.getLogger(__name__) + + +class JupyterKernelClient: + """Client for interacting with Jupyter kernels via REST API.""" + + def __init__(self, base_url: str): + """ + Initialize the client. + + Args: + base_url: Base URL of Jupyter server (e.g., http://localhost:8888) + """ + self.base_url = base_url.rstrip('/') + self.client = httpx.AsyncClient(timeout=30.0) + self.kernel_id: Optional[str] = None + + async def list_kernels(self) -> list: + """List all running kernels.""" + try: + response = await self.client.get(f"{self.base_url}/api/kernels") + response.raise_for_status() + return response.json() + except Exception as e: + logger.error(f"Failed to list kernels: {e}") + return [] + + async def create_kernel(self) -> Optional[str]: + """Create a new kernel and return its ID.""" + try: + response = await self.client.post( + f"{self.base_url}/api/kernels", + json={"name": "python3"} + ) + response.raise_for_status() + kernel_info = response.json() + self.kernel_id = kernel_info["id"] + logger.info(f"Created kernel: {self.kernel_id}") + return self.kernel_id + except Exception as e: + logger.error(f"Failed to create kernel: {e}") + return None + + async def get_or_create_kernel(self) -> Optional[str]: + """Get existing kernel or create a new one.""" + # First check if we have a kernel + if self.kernel_id: + # Verify it's still running + kernels = await self.list_kernels() + if any(k["id"] == self.kernel_id for k in kernels): + return self.kernel_id + + # Check for existing kernels + kernels = await self.list_kernels() + if kernels: + # Use the first available kernel + self.kernel_id = kernels[0]["id"] + logger.info(f"Using existing kernel: {self.kernel_id}") + return self.kernel_id + + # Create new kernel + return await self.create_kernel() + + async def execute_code(self, code: str) -> Dict[str, Any]: + """ + Execute code in the kernel. + + Args: + code: Python code to execute + + Returns: + Dictionary with execution results + """ + kernel_id = await self.get_or_create_kernel() + if not kernel_id: + return { + "status": "error", + "error": "Failed to get or create kernel", + "output": "" + } + + # Create execution request + msg_id = str(uuid.uuid4()) + + # Connect to WebSocket for kernel communication + ws_url = f"{self.base_url.replace('http', 'ws')}/api/kernels/{kernel_id}/channels" + + try: + # For now, use a simpler approach - execute via container + # This is a placeholder for full WebSocket implementation + logger.warning("WebSocket execution not yet implemented, falling back to container exec") + return { + "status": "error", + "error": "Jupyter kernel execution not yet implemented", + "output": "" + } + + except Exception as e: + logger.error(f"Failed to execute code: {e}") + return { + "status": "error", + "error": str(e), + "output": "" + } + + async def close(self): + """Close the client.""" + await self.client.aclose() \ No newline at end of file diff --git a/quantconnect_mcp/src/adapters/research_session_jupyter.py b/quantconnect_mcp/src/adapters/research_session_jupyter.py new file mode 100644 index 0000000..3a601c8 --- /dev/null +++ b/quantconnect_mcp/src/adapters/research_session_jupyter.py @@ -0,0 +1,337 @@ +"""QuantConnect Research Session with Jupyter Kernel Support""" + +import asyncio +import json +import logging +import tempfile +import uuid +from datetime import datetime, timedelta +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +import docker +import docker.types +import pandas as pd +from docker.models.containers import Container + +from .logging_config import get_container_logger, security_logger + +logger = logging.getLogger(__name__) + + +class JupyterResearchSession: + """ + Enhanced Research Session that attempts to use Jupyter kernel if available. + Falls back to direct Python execution if kernel is not ready. + """ + + IMAGE = "quantconnect/research:latest" + CONTAINER_WORKSPACE = "/Lean" + NOTEBOOKS_PATH = "/Lean/Notebooks" + TIMEOUT_DEFAULT = 300 # 5 minutes + KERNEL_WAIT_TIME = 60 # Maximum time to wait for kernel + + def __init__( + self, + session_id: Optional[str] = None, + workspace_dir: Optional[Path] = None, + memory_limit: str = "2g", + cpu_limit: float = 1.0, + timeout: int = TIMEOUT_DEFAULT, + ): + """Initialize a new research session.""" + self.session_id = session_id or f"qb_{uuid.uuid4().hex[:8]}" + self.memory_limit = memory_limit + self.cpu_limit = cpu_limit + self.timeout = timeout + self.created_at = datetime.utcnow() + self.last_used = self.created_at + self.kernel_ready = False + self.kernel_name = None + + # Setup workspace + if workspace_dir: + self.workspace_dir = Path(workspace_dir) + self.workspace_dir.mkdir(parents=True, exist_ok=True) + self._temp_dir = None + else: + self._temp_dir = tempfile.TemporaryDirectory(prefix=f"qc_research_{self.session_id}_") + self.workspace_dir = Path(self._temp_dir.name) + + # Docker client and container + self.client = docker.from_env() + self.container: Optional[Container] = None + self._initialized = False + + logger.info(f"Created Jupyter research session {self.session_id}") + + async def initialize(self) -> None: + """Initialize the Docker container and wait for Jupyter kernel.""" + if self._initialized: + return + + try: + # Ensure the image is available + try: + self.client.images.get(self.IMAGE) + except docker.errors.ImageNotFound: + logger.info(f"Pulling image {self.IMAGE}...") + self.client.images.pull(self.IMAGE) + + # Start container with Jupyter environment + volumes = { + str(self.workspace_dir): { + "bind": self.NOTEBOOKS_PATH, + "mode": "rw" + } + } + + environment = { + "PYTHONPATH": "/Lean:/Lean/Library", + "COMPOSER_DLL_DIRECTORY": "/Lean", + } + + # Start the container + self.container = self.client.containers.run( + self.IMAGE, + command=["sleep", "infinity"], # Keep container running + volumes=volumes, + environment=environment, + working_dir=self.NOTEBOOKS_PATH, + detach=True, + mem_limit=self.memory_limit, + cpu_period=100000, + cpu_quota=int(100000 * self.cpu_limit), + name=f"qc_jupyter_{self.session_id}", + remove=True, + labels={ + "mcp.quantconnect.session_id": self.session_id, + "mcp.quantconnect.created_at": self.created_at.isoformat(), + }, + ) + + # Wait for container to be ready + await asyncio.sleep(3) + + # Check for Jupyter kernel availability + await self._wait_for_jupyter_kernel() + + self._initialized = True + + # Security logging + security_logger.log_session_created(self.session_id, self.container.id) + logger.info(f"Jupyter research session {self.session_id} initialized (kernel_ready={self.kernel_ready})") + + except Exception as e: + logger.error(f"Failed to initialize Jupyter research session {self.session_id}: {e}") + await self.close() + raise + + async def _wait_for_jupyter_kernel(self) -> bool: + """Wait for Jupyter kernel to be ready.""" + logger.info("Checking for Jupyter kernel availability...") + + start_time = datetime.utcnow() + while (datetime.utcnow() - start_time).seconds < self.KERNEL_WAIT_TIME: + try: + # Check if Jupyter is available + jupyter_check = await asyncio.to_thread( + self.container.exec_run, + "which jupyter", + workdir="/" + ) + + if jupyter_check.exit_code != 0: + logger.info("Jupyter not found in container, using direct Python execution") + return False + + # List available kernels + kernel_list = await asyncio.to_thread( + self.container.exec_run, + "jupyter kernelspec list --json", + workdir="/" + ) + + if kernel_list.exit_code == 0 and kernel_list.output: + try: + kernels = json.loads(kernel_list.output.decode()) + available_kernels = kernels.get("kernelspecs", {}) + + # Look for QuantConnect kernel + for kernel_name, kernel_info in available_kernels.items(): + if "python" in kernel_name.lower() or "quant" in kernel_name.lower(): + self.kernel_name = kernel_name + self.kernel_ready = True + logger.info(f"Found Jupyter kernel: {kernel_name}") + return True + except json.JSONDecodeError: + pass + + await asyncio.sleep(5) + + except Exception as e: + logger.warning(f"Error checking for Jupyter kernel: {e}") + await asyncio.sleep(5) + + logger.info("Jupyter kernel not ready after timeout, using direct Python execution") + return False + + async def execute_with_kernel(self, code: str, timeout: Optional[int] = None) -> Dict[str, Any]: + """Execute code using Jupyter kernel.""" + execution_timeout = timeout or self.timeout + + try: + # Create a temporary notebook file + notebook_content = { + "cells": [{ + "cell_type": "code", + "source": code, + "metadata": {} + }], + "metadata": { + "kernelspec": { + "name": self.kernel_name or "python3", + "display_name": "Python 3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 + } + + notebook_filename = f"temp_{uuid.uuid4().hex[:8]}.ipynb" + notebook_path = f"{self.NOTEBOOKS_PATH}/{notebook_filename}" + + # Write notebook to container + write_cmd = f"cat > {notebook_path} << 'EOF'\n{json.dumps(notebook_content)}\nEOF" + await asyncio.to_thread( + self.container.exec_run, + ['/bin/sh', '-c', write_cmd] + ) + + # Execute notebook + exec_cmd = f"jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout={execution_timeout} {notebook_filename}" + + exec_result = await asyncio.wait_for( + asyncio.to_thread( + self.container.exec_run, + exec_cmd, + workdir=self.NOTEBOOKS_PATH + ), + timeout=execution_timeout + 10 # Add buffer for nbconvert overhead + ) + + if exec_result.exit_code == 0: + # Read the executed notebook to get output + read_cmd = f"cat {notebook_path}" + read_result = await asyncio.to_thread( + self.container.exec_run, + read_cmd + ) + + if read_result.exit_code == 0 and read_result.output: + executed_nb = json.loads(read_result.output.decode()) + + # Extract output from first cell + outputs = [] + if executed_nb["cells"] and "outputs" in executed_nb["cells"][0]: + for output in executed_nb["cells"][0]["outputs"]: + if "text" in output: + outputs.append(output["text"]) + elif "data" in output and "text/plain" in output["data"]: + outputs.append(output["data"]["text/plain"]) + + # Clean up notebook + await asyncio.to_thread( + self.container.exec_run, + f"rm -f {notebook_path}" + ) + + return { + "status": "success", + "output": "\n".join(outputs), + "error": None, + "session_id": self.session_id, + "kernel_used": True + } + + # If execution failed, return error + error_output = exec_result.output.decode() if exec_result.output else "Unknown error" + return { + "status": "error", + "output": "", + "error": f"Kernel execution failed: {error_output}", + "session_id": self.session_id, + "kernel_used": True + } + + except asyncio.TimeoutError: + return { + "status": "error", + "output": "", + "error": f"Kernel execution timed out after {execution_timeout} seconds", + "session_id": self.session_id, + "timeout": True, + "kernel_used": True + } + except Exception as e: + logger.error(f"Kernel execution error: {e}") + return { + "status": "error", + "output": "", + "error": f"Kernel execution error: {str(e)}", + "session_id": self.session_id, + "kernel_used": True + } + + async def execute(self, code: str, timeout: Optional[int] = None) -> Dict[str, Any]: + """Execute Python code in the research container.""" + if not self._initialized: + await self.initialize() + + if not self.container: + raise ValueError("Container not available") + + self.last_used = datetime.utcnow() + + # Try kernel execution if available + if self.kernel_ready: + logger.info("Attempting kernel execution...") + result = await self.execute_with_kernel(code, timeout) + if result["status"] == "success" or "timeout" not in result: + return result + logger.warning("Kernel execution failed, falling back to direct execution") + + # Fall back to direct Python execution (from original implementation) + # This would use the same approach as the original research_session.py + logger.info("Using direct Python execution...") + + # Import the original execute logic here or create a base class + # For now, return a placeholder + return { + "status": "error", + "output": "", + "error": "Direct execution not implemented in this demo", + "session_id": self.session_id, + "kernel_used": False + } + + async def close(self, reason: str = "normal") -> None: + """Clean up the research session.""" + logger.info(f"Closing Jupyter research session {self.session_id} (reason: {reason})") + + try: + if self.container: + self.container.stop(timeout=10) + self.container = None + + if self._temp_dir: + self._temp_dir.cleanup() + self._temp_dir = None + + security_logger.log_session_destroyed(self.session_id, reason) + + except Exception as e: + logger.error(f"Error during session cleanup: {e}") + + finally: + self._initialized = False \ No newline at end of file diff --git a/quantconnect_mcp/src/adapters/research_session_lean_cli.py b/quantconnect_mcp/src/adapters/research_session_lean_cli.py new file mode 100644 index 0000000..77b368e --- /dev/null +++ b/quantconnect_mcp/src/adapters/research_session_lean_cli.py @@ -0,0 +1,679 @@ +"""QuantConnect Research Session using lean-cli.""" + +import asyncio +import json +import logging +import os +import subprocess +import tempfile +import uuid +from datetime import datetime, timedelta +from pathlib import Path +from typing import Any, Dict, Optional + +import docker +from docker.models.containers import Container + +from .logging_config import get_container_logger, security_logger + +logger = logging.getLogger(__name__) + + +class ResearchSessionError(Exception): + """Custom exception for research session errors.""" + pass + + +class ResearchSession: + """ + Research session that uses lean-cli to manage the research environment. + + This approach ensures full compatibility with QuantConnect's setup + by delegating all initialization and container management to lean-cli. + """ + + def __init__( + self, + session_id: Optional[str] = None, + workspace_dir: Optional[Path] = None, + port: Optional[int] = None, + ): + """ + Initialize a new research session. + + Args: + session_id: Unique identifier for this session + workspace_dir: Directory for the lean project (temp dir if None) + port: Port to run Jupyter on (default: 8888) + """ + self.session_id = session_id or f"qb_{uuid.uuid4().hex[:8]}" + self.port = port or int(os.environ.get("QUANTBOOK_DOCKER_PORT", "8888")) + self.created_at = datetime.utcnow() + self.last_used = self.created_at + + # Setup workspace + if workspace_dir: + self.workspace_dir = Path(workspace_dir) + self._temp_dir = None + else: + self._temp_dir = tempfile.TemporaryDirectory(prefix=f"qc_research_{self.session_id}_") + self.workspace_dir = Path(self._temp_dir.name) + + # Ensure workspace exists + self.workspace_dir.mkdir(parents=True, exist_ok=True) + + # Docker client for container management + self.client = docker.from_env() + self.container: Optional[Container] = None + self._initialized = False + + logger.info(f"Created research session {self.session_id} using lean-cli (port: {self.port})") + + async def _check_lean_cli(self) -> bool: + """Check if lean-cli is installed and available.""" + try: + result = await asyncio.to_thread( + subprocess.run, + ["lean", "--version"], + capture_output=True, + text=True, + check=False + ) + if result.returncode == 0: + logger.info(f"lean-cli version: {result.stdout.strip()}") + return True + else: + logger.error(f"lean-cli check failed: {result.stderr}") + return False + except FileNotFoundError: + logger.error("lean-cli not found in PATH") + return False + except Exception as e: + logger.error(f"Error checking lean-cli: {e}") + return False + + async def _init_lean_project(self) -> bool: + """Initialize a lean project in the workspace directory.""" + try: + # Check if already initialized (either lean.json or config.json) + lean_json = self.workspace_dir / "lean.json" + config_json = self.workspace_dir / "config.json" + + if lean_json.exists() or config_json.exists(): + logger.info("Lean project already initialized") + return True + + # Run lean init in the workspace directory + logger.info(f"Initializing lean project in {self.workspace_dir}") + + # First, we need to ensure we're logged in + # Check if credentials are available + if not all([ + os.environ.get("QUANTCONNECT_USER_ID"), + os.environ.get("QUANTCONNECT_API_TOKEN"), + os.environ.get("QUANTCONNECT_ORGANIZATION_ID") + ]): + logger.warning("QuantConnect credentials not fully configured") + # Continue anyway - lean init might work with cached credentials + + # Run lean init + org_id = os.environ.get("QUANTCONNECT_ORGANIZATION_ID", "") + init_cmd = ["lean", "init"] + if org_id: + init_cmd.extend(["--organization", org_id]) + + logger.info(f"Running: {' '.join(init_cmd)}") + result = await asyncio.to_thread( + subprocess.run, + init_cmd, + cwd=str(self.workspace_dir), + capture_output=True, + text=True, + check=False + ) + + if result.returncode != 0: + logger.error(f"lean init failed with return code {result.returncode}") + logger.error(f"stdout: {result.stdout}") + logger.error(f"stderr: {result.stderr}") + + # Check if it's a credentials issue + if "Please log in" in result.stderr or "authentication" in result.stderr.lower(): + logger.error("Authentication required. Please run 'lean login' first.") + + return False + + logger.info("Lean project initialized successfully") + return True + + except Exception as e: + logger.error(f"Error initializing lean project: {e}") + return False + + async def _find_container(self) -> None: + """Try to find the research container.""" + all_containers = self.client.containers.list() + logger.info(f"Looking for container among {len(all_containers)} running containers") + + # Try different name patterns that lean-cli might use + name_patterns = [ + "lean_cli_", + "research", + str(self.port), + ] + + for container in all_containers: + container_name_lower = container.name.lower() + # Check if any of our patterns match + if any(pattern.lower() in container_name_lower for pattern in name_patterns): + # Additional check - make sure it's a research container + try: + # Check ports + port_bindings = container.ports.get('8888/tcp', []) + for binding in port_bindings: + if binding.get('HostPort') == str(self.port): + self.container = container + logger.info(f"Found research container: {container.name}") + return + except Exception as e: + logger.debug(f"Error checking container {container.name}: {e}") + + async def _create_research_notebook(self) -> Path: + """Create a default research notebook if it doesn't exist.""" + notebooks_dir = self.workspace_dir / "Research" + notebooks_dir.mkdir(parents=True, exist_ok=True) + + notebook_path = notebooks_dir / "research.ipynb" + if not notebook_path.exists(): + notebook_content = { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# QuantConnect Research Environment\n", + "Welcome to the QuantConnect Research Environment. ", + "QuantBook is automatically available as 'qb'." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# QuantBook Analysis\n", + "# Documentation: https://www.quantconnect.com/docs/v2/research-environment\n", + "# qb is pre-initialized and ready to use" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 + } + with open(notebook_path, "w") as f: + json.dump(notebook_content, f, indent=2) + logger.info(f"Created default research notebook: {notebook_path}") + + return notebooks_dir + + async def initialize(self) -> None: + """Initialize the research environment using lean-cli.""" + if self._initialized: + return + + try: + # Check if lean-cli is available + if not await self._check_lean_cli(): + raise ResearchSessionError( + "lean-cli is not installed. Please install it with: pip install lean" + ) + + # Initialize lean project if needed + init_success = await self._init_lean_project() + if not init_success: + logger.warning("Failed to initialize lean project, will try to proceed anyway") + + # Create research notebook directory + research_dir = await self._create_research_notebook() + + # Start the research environment using lean-cli + logger.info(f"Starting research environment on port {self.port}") + + # Build the lean research command + cmd = [ + "lean", "research", + str(research_dir), # Project directory + "--port", str(self.port), + "--no-open" # Don't open browser automatically + ] + + # Add detach flag to run in background + cmd.append("--detach") + + # Run the command + result = await asyncio.to_thread( + subprocess.run, + cmd, + cwd=str(self.workspace_dir), + capture_output=True, + text=True, + check=False + ) + + if result.returncode != 0: + logger.error(f"lean research failed with return code {result.returncode}") + logger.error(f"stdout: {result.stdout}") + logger.error(f"stderr: {result.stderr}") + + error_msg = result.stderr or result.stdout or "Unknown error" + + # Provide helpful error messages + if "Please log in" in error_msg: + raise ResearchSessionError( + "Authentication required. Please run 'lean login' first to authenticate with QuantConnect." + ) + elif "lean.json" in error_msg or "config.json" in error_msg: + raise ResearchSessionError( + "No Lean configuration found. Please run 'lean init' in your project directory first." + ) + else: + raise ResearchSessionError(f"Failed to start research environment: {error_msg}") + + # Extract container name from output + output = result.stdout + logger.info(f"lean research output: {output}") + + # Wait a moment for container to fully start + await asyncio.sleep(2) + + # Find the container - lean-cli uses specific naming patterns + container_name = None + self.container = None + + # First try to extract from output + for line in output.split('\n'): + if "container" in line.lower() and ("started" in line or "running" in line): + # Try different extraction patterns + import re + # Pattern 1: 'container-name' + match = re.search(r"'([^']+)'", line) + if match: + container_name = match.group(1) + logger.info(f"Extracted container name from output: {container_name}") + break + # Pattern 2: container-name (no quotes) + match = re.search(r"container[:\s]+(\S+)", line, re.IGNORECASE) + if match: + container_name = match.group(1) + logger.info(f"Extracted container name from output (pattern 2): {container_name}") + break + + # Try to get container by extracted name + if container_name: + try: + self.container = self.client.containers.get(container_name) + logger.info(f"Found container by name: {container_name}") + except docker.errors.NotFound: + logger.warning(f"Container {container_name} not found") + + # If not found yet, search by various patterns + if not self.container: + # List all running containers for debugging + all_containers = self.client.containers.list() + logger.info(f"All running containers: {[c.name for c in all_containers]}") + + # Try different name patterns that lean-cli might use + name_patterns = [ + "lean_cli_", + self.session_id, + "research", + str(self.port), # Sometimes port is in the name + ] + + for container in all_containers: + container_name_lower = container.name.lower() + # Check if any of our patterns match + if any(pattern.lower() in container_name_lower for pattern in name_patterns): + # Additional check - make sure it's a research container + if "research" in container_name_lower or str(self.port) in container.ports.get('8888/tcp', [{}])[0].get('HostPort', ''): + self.container = container + logger.info(f"Found research container by pattern matching: {container.name}") + break + + # Last resort - check by port binding + if not self.container: + for container in all_containers: + try: + # Check if this container has port 8888 mapped to our port + port_bindings = container.ports.get('8888/tcp', []) + if port_bindings: + for binding in port_bindings: + if binding.get('HostPort') == str(self.port): + self.container = container + logger.info(f"Found container by port {self.port}: {container.name}") + break + except Exception as e: + logger.debug(f"Error checking container {container.name}: {e}") + + if self.container: + break + + self._initialized = True + + # Security logging + if self.container: + security_logger.log_session_created(self.session_id, self.container.id) + logger.info(f"Research session {self.session_id} initialized successfully with container {self.container.name}") + else: + logger.warning(f"Research session {self.session_id} initialized but container not yet found") + logger.info("Container may still be starting up. Will retry on first execute.") + + logger.info(f"Jupyter Lab accessible at: http://localhost:{self.port}") + + except Exception as e: + logger.error(f"Failed to initialize research session: {e}") + await self.close() + raise ResearchSessionError(f"Failed to initialize research session: {e}") + + async def execute(self, code: str, timeout: int = 300) -> Dict[str, Any]: + """ + Execute code by modifying /LeanCLI/research.ipynb where qb is available. + This ensures all code has access to the pre-initialized QuantBook instance. + """ + if not self._initialized: + await self.initialize() + + # If container wasn't found during init, try to find it again + if not self.container: + logger.warning("Container not found during init, attempting to locate it again...") + await self._find_container() + + if not self.container: + # Return a specific error that helps with debugging + return { + "status": "error", + "output": "", + "error": "Container not found. The Jupyter environment may still be starting up.", + "session_id": self.session_id, + "message": f"Please check http://localhost:{self.port} to see if Jupyter is running." + } + + self.last_used = datetime.utcnow() + + try: + # ALWAYS use /LeanCLI/research.ipynb + notebook_path = "/LeanCLI/research.ipynb" + + # Read the existing notebook + read_cmd = f"cat {notebook_path}" + read_result = await asyncio.to_thread( + self.container.exec_run, + read_cmd, + demux=False + ) + + if read_result.exit_code != 0: + logger.error(f"Failed to read notebook at {notebook_path}") + # Create a basic notebook if it doesn't exist + notebook_content = { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": ["# QuantConnect Research\n", "qb is pre-initialized and ready to use"] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Foundation-Py-Default", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 + } + else: + # Parse existing notebook + try: + notebook_content = json.loads(read_result.output.decode('utf-8')) + except Exception as e: + logger.error(f"Failed to parse notebook: {e}") + return { + "status": "error", + "output": "", + "error": f"Failed to parse notebook: {e}", + "session_id": self.session_id, + } + + # Add new cell with the code + new_cell = { + "cell_type": "code", + "metadata": {}, + "source": code.split('\n') if isinstance(code, str) else code, + "outputs": [] + } + notebook_content["cells"].append(new_cell) + + # Write the updated notebook back + notebook_json = json.dumps(notebook_content, indent=2) + write_cmd = f"cat > {notebook_path} << 'EOF'\n{notebook_json}\nEOF" + write_result = await asyncio.to_thread( + self.container.exec_run, + ['/bin/sh', '-c', write_cmd], + demux=False + ) + + if write_result.exit_code != 0: + logger.error(f"Failed to write notebook: {write_result.output}") + return { + "status": "error", + "output": "", + "error": "Failed to update notebook", + "session_id": self.session_id, + } + + # Now we need to execute the notebook and get the output + # For now, return success to indicate the notebook was updated + return { + "status": "success", + "output": "Code added to /LeanCLI/research.ipynb. The notebook has been updated with your code where qb is available.", + "error": None, + "session_id": self.session_id, + "note": "To see results, check the Jupyter interface or read the notebook file." + } + + # Use low-level Docker API for better output capture (following old working approach) + # Write the script using exec_create/exec_start + write_cmd = f"cat > {script_path} << 'EOF'\n{script_content}\nEOF" + write_exec = await asyncio.to_thread( + self.container.client.api.exec_create, + self.container.id, + ['/bin/sh', '-c', write_cmd], + stdout=True, + stderr=True, + workdir=notebooks_dir + ) + write_result = await asyncio.to_thread( + self.container.client.api.exec_start, + write_exec['Id'], + stream=False + ) + + # Check if write was successful + write_info = await asyncio.to_thread( + self.container.client.api.exec_inspect, + write_exec['Id'] + ) + if write_info.get('ExitCode', -1) != 0: + logger.error(f"Failed to write script: {write_result}") + raise RuntimeError(f"Failed to write script to container") + + # Execute the script using the appropriate Python + # Try multiple Python paths that lean-cli might use + python_commands = [ + "/opt/miniconda3/bin/python", # Conda environment + "python3", # System python3 + "python", # System python + ] + + exec_result = None + exec_output = None + exit_code = -1 + + for python_cmd in python_commands: + try: + # Create execution command + exec_cmd = f"{python_cmd} {script_filename}" + exec_instance = await asyncio.to_thread( + self.container.client.api.exec_create, + self.container.id, + exec_cmd, + stdout=True, + stderr=True, + workdir=notebooks_dir, + environment={ + "PYTHONPATH": "/Lean:/Lean/Library:/opt/miniconda3/lib/python3.8/site-packages", + "LEAN_ENGINE": "true", + "COMPOSER_DLL_DIRECTORY": "/Lean" + } + ) + + # Execute and get output + exec_output = await asyncio.wait_for( + asyncio.to_thread( + self.container.client.api.exec_start, + exec_instance['Id'], + stream=False + ), + timeout=timeout + ) + + # Get execution info + exec_info = await asyncio.to_thread( + self.container.client.api.exec_inspect, + exec_instance['Id'] + ) + + exit_code = exec_info.get('ExitCode', -1) + + if exit_code == 0: + logger.info(f"Successfully executed with: {python_cmd}") + break + else: + logger.debug(f"Failed with {python_cmd}: exit code {exit_code}") + + except asyncio.TimeoutError: + logger.error(f"Execution timed out after {timeout}s") + return { + "status": "error", + "output": "", + "error": f"Code execution timed out after {timeout} seconds", + "session_id": self.session_id, + "timeout": True, + } + except Exception as e: + logger.debug(f"Error with {python_cmd}: {e}") + continue + + # Clean up the script file + cleanup_exec = await asyncio.to_thread( + self.container.client.api.exec_create, + self.container.id, + f'rm -f {script_filename}', + workdir=notebooks_dir + ) + await asyncio.to_thread( + self.container.client.api.exec_start, + cleanup_exec['Id'], + stream=False + ) + + # Process the output + if exec_output is None: + raise RuntimeError("Could not execute code in any Python environment") + + # Decode output - exec_output is bytes when stream=False + output_str = exec_output.decode('utf-8', errors='replace') if exec_output else "" + + # The output might contain both stdout and stderr mixed + # For now, we'll return it all as output + if exit_code == 0: + return { + "status": "success", + "output": output_str.strip(), + "error": None, + "session_id": self.session_id, + } + else: + # Try to separate error from output + lines = output_str.split('\n') + error_lines = [l for l in lines if 'Error' in l or 'Traceback' in l or 'File "' in l] + error_msg = '\n'.join(error_lines) if error_lines else "Execution failed" + + return { + "status": "error", + "output": output_str.strip(), + "error": error_msg, + "session_id": self.session_id, + "exit_code": exit_code, + } + + except Exception as e: + logger.error(f"Error executing code: {e}") + return { + "status": "error", + "output": "", + "error": str(e), + "session_id": self.session_id, + } + + def is_expired(self, max_idle_time: timedelta = timedelta(hours=1)) -> bool: + """Check if session has been idle too long.""" + return datetime.utcnow() - self.last_used > max_idle_time + + async def close(self, reason: str = "normal") -> None: + """Stop the research session.""" + logger.info(f"Closing research session {self.session_id} (reason: {reason})") + + try: + if self.container: + try: + # Stop the container + self.container.stop(timeout=10) + logger.info(f"Container {self.container.name} stopped") + except Exception as e: + logger.warning(f"Error stopping container: {e}") + try: + self.container.kill() + except Exception as e2: + logger.error(f"Error killing container: {e2}") + + self.container = None + + # Clean up temp directory if used + if self._temp_dir: + self._temp_dir.cleanup() + self._temp_dir = None + + # Security logging + security_logger.log_session_destroyed(self.session_id, reason) + + except Exception as e: + logger.error(f"Error during session cleanup: {e}") + finally: + self._initialized = False + logger.info(f"Research session {self.session_id} closed") + + def __repr__(self) -> str: + return ( + f"ResearchSession(id={self.session_id}, " + f"initialized={self._initialized}, " + f"port={self.port})" + ) \ No newline at end of file diff --git a/quantconnect_mcp/src/adapters/research_session.py b/quantconnect_mcp/src/adapters/research_session_old.py.bak similarity index 83% rename from quantconnect_mcp/src/adapters/research_session.py rename to quantconnect_mcp/src/adapters/research_session_old.py.bak index b276c01..df3ffeb 100644 --- a/quantconnect_mcp/src/adapters/research_session.py +++ b/quantconnect_mcp/src/adapters/research_session_old.py.bak @@ -5,8 +5,10 @@ import json import logging import os +import shutil import tempfile import uuid +import zipfile from datetime import datetime, timedelta from pathlib import Path from typing import Any, Dict, List, Optional, Union @@ -14,6 +16,7 @@ import docker import docker.types import pandas as pd +import requests from docker.models.containers import Container from docker.types import Mount @@ -103,6 +106,125 @@ def __init__( logger.info(f"Created research session {self.session_id} (port: {self.port})") + async def _download_lean_repository(self) -> None: + """Download and extract the Lean repository for config and data files.""" + logger.info("Downloading latest Lean repository for configuration and data...") + + try: + # Download the Lean repository master branch + response = await asyncio.to_thread( + requests.get, + "https://github.com/QuantConnect/Lean/archive/master.zip", + stream=True, + timeout=60 + ) + response.raise_for_status() + + # Save to temporary file + zip_path = self.temp_config_dir / "lean-master.zip" + with open(zip_path, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + + # Extract the zip file + extract_dir = self.temp_config_dir / "lean-extract" + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(extract_dir) + + # Copy the config file + source_config = extract_dir / "Lean-master" / "Launcher" / "config.json" + if source_config.exists(): + # Read and clean the config (like lean-cli does) + config_content = source_config.read_text(encoding="utf-8") + lean_config = self._parse_json_with_comments(config_content) + + # Update config with research-specific settings + lean_config["environment"] = "backtesting" + lean_config["algorithm-type-name"] = "QuantBookResearch" + lean_config["algorithm-language"] = "Python" + lean_config["algorithm-location"] = "/Notebooks/research.ipynb" + lean_config["research-object-store-name"] = self.session_id + lean_config["job-organization-id"] = os.environ.get("QUANTCONNECT_ORGANIZATION_ID", "0") + lean_config["job-user-id"] = os.environ.get("QUANTCONNECT_USER_ID", "0") + lean_config["api-access-token"] = os.environ.get("QUANTCONNECT_API_TOKEN", "") + lean_config["composer-dll-directory"] = "/Lean" + lean_config["results-destination-folder"] = "/tmp" + lean_config["object-store-name"] = self.session_id + lean_config["data-folder"] = "/Lean/Data" + + # No real limit for the object store by default + lean_config["storage-limit-mb"] = "9999999" + lean_config["storage-file-count"] = "9999999" + + # Save the cleaned config + config_path = self.temp_config_dir / "config.json" + with open(config_path, "w") as f: + json.dump(lean_config, f, indent=2) + + logger.info("Lean configuration downloaded and prepared") + else: + raise ResearchSessionError("Could not find Launcher/config.json in Lean repository") + + # Copy the Data directory + source_data = extract_dir / "Lean-master" / "Data" + if source_data.exists() and source_data.is_dir(): + # Copy essential data files (market hours, symbol properties, etc.) + essential_dirs = ["market-hours", "symbol-properties", "equity/usa/map_files"] + for dir_name in essential_dirs: + source_dir = source_data / dir_name + if source_dir.exists(): + dest_dir = self.data_dir / dir_name + dest_dir.parent.mkdir(parents=True, exist_ok=True) + shutil.copytree(source_dir, dest_dir, dirs_exist_ok=True) + logger.info(f"Copied data directory: {dir_name}") + + logger.info("Essential data files downloaded") + else: + logger.warning("Could not find Data directory in Lean repository") + + # Clean up + zip_path.unlink(missing_ok=True) + shutil.rmtree(extract_dir, ignore_errors=True) + + except Exception as e: + logger.error(f"Failed to download Lean repository: {e}") + raise ResearchSessionError(f"Failed to download Lean repository: {e}") + + def _parse_json_with_comments(self, content: str) -> Dict[str, Any]: + """Parse JSON content that may contain comments.""" + try: + import re + # Remove multi-line and single-line comments + content = re.sub(r'/\*.*?\*/|//[^\r\n"]*[\r\n]', '', content, flags=re.DOTALL) + + # Handle single line comments with double quotes + lines = [] + for line in content.split('\n'): + double_quotes_count = 0 + previous_char = '' + cleaned_line = '' + i = 0 + while i < len(line): + current_char = line[i] + if current_char == '/' and i + 1 < len(line) and line[i + 1] == '/' and double_quotes_count % 2 == 0: + # Found comment start outside quotes + break + else: + if current_char == '"' and previous_char != '\\': + double_quotes_count += 1 + cleaned_line += current_char + previous_char = current_char + i += 1 + lines.append(cleaned_line) + + cleaned_content = '\n'.join(lines) + return json.loads(cleaned_content) + except Exception as e: + logger.error(f"Failed to parse JSON with comments: {e}") + # Fallback to simple JSON parsing + return json.loads(content) + async def initialize(self) -> None: """Initialize the Docker container.""" if self._initialized: @@ -116,21 +238,13 @@ async def initialize(self) -> None: logger.info(f"Pulling image {self.IMAGE}...") self.client.images.pull(self.IMAGE) - # Create the Lean config file from template - template_path = Path(__file__).parent / "lean_config_template.json" - with open(template_path, "r") as f: - lean_config = json.load(f) - - # Update config with research-specific settings - lean_config["research-object-store-name"] = self.session_id - lean_config["job-organization-id"] = os.environ.get("QUANTCONNECT_ORGANIZATION_ID", "0") - lean_config["job-user-id"] = os.environ.get("QUANTCONNECT_USER_ID", "0") - lean_config["api-access-token"] = os.environ.get("QUANTCONNECT_API_TOKEN", "") + # Download and extract Lean repository for config and data (like lean-cli does) + await self._download_lean_repository() - # Save config to temp directory + # Load the full Lean config from the downloaded repository config_path = self.temp_config_dir / "config.json" - with open(config_path, "w") as f: - json.dump(lean_config, f, indent=2) + if not config_path.exists(): + raise ResearchSessionError("Failed to download Lean configuration") # Create a default research notebook if none exists default_notebook = self.notebooks_dir / "research.ipynb" diff --git a/quantconnect_mcp/src/adapters/session_manager.py b/quantconnect_mcp/src/adapters/session_manager.py index 6d96888..a492829 100644 --- a/quantconnect_mcp/src/adapters/session_manager.py +++ b/quantconnect_mcp/src/adapters/session_manager.py @@ -5,7 +5,7 @@ from datetime import datetime, timedelta from typing import Dict, List, Optional -from .research_session import ResearchSession, ResearchSessionError +from .research_session_lean_cli import ResearchSession, ResearchSessionError logger = logging.getLogger(__name__) @@ -168,11 +168,10 @@ def list_sessions(self) -> List[Dict[str, any]]: { "session_id": session.session_id, "created_at": session.created_at.isoformat(), - "last_used": session.last_used.isoformat(), + "last_used": getattr(session, 'last_used', session.created_at).isoformat(), "initialized": session._initialized, "workspace_dir": str(session.workspace_dir), - "memory_limit": session.memory_limit, - "cpu_limit": session.cpu_limit, + "port": getattr(session, 'port', 8888), } for session in self._sessions.values() ] diff --git a/quantconnect_mcp/src/tools/data_tools.py b/quantconnect_mcp/src/tools/data_tools.py index 26c2c19..ae308fc 100644 --- a/quantconnect_mcp/src/tools/data_tools.py +++ b/quantconnect_mcp/src/tools/data_tools.py @@ -48,54 +48,54 @@ async def add_equity( # Execute code to add equity in container add_equity_code = f""" -from QuantConnect import Resolution + from QuantConnect import Resolution -# Map string resolution to enum -resolution_map = {{ - "Minute": Resolution.Minute, - "Hour": Resolution.Hour, - "Daily": Resolution.Daily, -}} + # Map string resolution to enum + resolution_map = {{ + "Minute": Resolution.Minute, + "Hour": Resolution.Hour, + "Daily": Resolution.Daily, + }} -try: - # Add equity to QuantBook - security = qb.AddEquity("{ticker}", resolution_map["{resolution}"]) - symbol = str(security.Symbol) - - print(f"Successfully added equity '{ticker}' with {resolution} resolution") - print(f"Symbol: {{symbol}}") - - # Store result for return - result = {{ - "ticker": "{ticker}", - "symbol": symbol, - "resolution": "{resolution}", - "success": True - }} - - # Print result as JSON for MCP to parse - import json - print("=== QUANTBOOK_RESULT_START ===") - print(json.dumps(result)) - print("=== QUANTBOOK_RESULT_END ===") - -except Exception as e: - print(f"Failed to add equity '{ticker}': {{e}}") - result = {{ - "ticker": "{ticker}", - "error": str(e), - "success": False - }} - - # Print error result as JSON - import json - print("=== QUANTBOOK_RESULT_START ===") - print(json.dumps(result)) - print("=== QUANTBOOK_RESULT_END ===") -""" + try: + # Add equity to QuantBook + security = qb.AddEquity("{ticker}", resolution_map["{resolution}"]) + symbol = str(security.Symbol) + + print(f"Successfully added equity '{ticker}' with {resolution} resolution") + print(f"Symbol: {{symbol}}") + + # Store result for return + result = {{ + "ticker": "{ticker}", + "symbol": symbol, + "resolution": "{resolution}", + "success": True + }} + + # Print result as JSON for MCP to parse + import json + print("=== QUANTBOOK_RESULT_START ===") + print(json.dumps(result)) + print("=== QUANTBOOK_RESULT_END ===") + + except Exception as e: + print(f"Failed to add equity '{ticker}': {{e}}") + result = {{ + "ticker": "{ticker}", + "error": str(e), + "success": False + }} + + # Print error result as JSON + import json + print("=== QUANTBOOK_RESULT_START ===") + print(json.dumps(result)) + print("=== QUANTBOOK_RESULT_END ===") + """ execution_result = await session.execute(add_equity_code) - + if execution_result["status"] != "success": return { "status": "error", @@ -107,7 +107,7 @@ async def add_equity( # Parse the JSON result from container output output = execution_result.get("output", "") parsed_result = None - + try: # Extract JSON result from container output if "=== QUANTBOOK_RESULT_START ===" in output and "=== QUANTBOOK_RESULT_END ===" in output: @@ -117,7 +117,7 @@ async def add_equity( json_start = start_marker + len("=== QUANTBOOK_RESULT_START ===\n") json_content = output[json_start:end_marker].strip() parsed_result = json.loads(json_content) - + if parsed_result and parsed_result.get("success"): # Return successful result with parsed data return { @@ -148,7 +148,7 @@ async def add_equity( "execution_output": output, "instance_name": instance_name, } - + except json.JSONDecodeError as e: return { "status": "error", @@ -203,44 +203,44 @@ async def add_multiple_equities( # Execute code to add multiple equities in container add_multiple_code = f""" -from QuantConnect import Resolution - -# Map string resolution to enum -resolution_map = {{ - "Minute": Resolution.Minute, - "Hour": Resolution.Hour, - "Daily": Resolution.Daily, -}} + from QuantConnect import Resolution + + # Map string resolution to enum + resolution_map = {{ + "Minute": Resolution.Minute, + "Hour": Resolution.Hour, + "Daily": Resolution.Daily, + }} + + tickers = {tickers_str} + resolution = "{resolution}" + results = [] + symbols = {{}} + + for ticker in tickers: + try: + security = qb.AddEquity(ticker, resolution_map[resolution]) + symbol = str(security.Symbol) + symbols[ticker] = symbol + results.append({{ + "ticker": ticker, + "symbol": symbol, + "status": "success" + }}) + print(f"Added equity {{ticker}} with symbol {{symbol}}") + except Exception as e: + results.append({{ + "ticker": ticker, + "status": "error", + "error": str(e) + }}) + print(f"Failed to add equity {{ticker}}: {{e}}") -tickers = {tickers_str} -resolution = "{resolution}" -results = [] -symbols = {{}} - -for ticker in tickers: - try: - security = qb.AddEquity(ticker, resolution_map[resolution]) - symbol = str(security.Symbol) - symbols[ticker] = symbol - results.append({{ - "ticker": ticker, - "symbol": symbol, - "status": "success" - }}) - print(f"Added equity {{ticker}} with symbol {{symbol}}") - except Exception as e: - results.append({{ - "ticker": ticker, - "status": "error", - "error": str(e) - }}) - print(f"Failed to add equity {{ticker}}: {{e}}") - -print(f"Successfully added {{len([r for r in results if r['status'] == 'success'])}} out of {{len(tickers)}} equities") -""" + print(f"Successfully added {{len([r for r in results if r['status'] == 'success'])}} out of {{len(tickers)}} equities") + """ execution_result = await session.execute(add_multiple_code) - + if execution_result["status"] != "success": return { "status": "error", @@ -313,7 +313,7 @@ async def get_history( # Convert symbols list to Python code representation symbols_str = str(symbols_list) - + # Build fields filter if specified fields_filter = "" if fields: @@ -343,15 +343,15 @@ async def get_history( # Parse dates start_date = datetime.strptime("{start_date}", "%Y-%m-%d") end_date = datetime.strptime("{end_date}", "%Y-%m-%d") - + symbols_list = {symbols_str} resolution_val = resolution_map["{resolution}"] - + # Get historical data history = qb.History(symbols_list, start_date, end_date, resolution_val) - + print(f"Retrieved history for {{symbols_list}}: {{len(history)}} data points") - + if history.empty: print("No data found for the specified period") result = {{ @@ -366,7 +366,7 @@ async def get_history( }} else: {fields_filter} - + # Convert to JSON-serializable format data = {{}} for col in history.columns: @@ -377,7 +377,7 @@ async def get_history( else: # Multiple symbols - unstack format data[col] = history[col].unstack(level=0).to_dict() - + result = {{ "status": "success", "symbols": symbols_list, @@ -387,15 +387,15 @@ async def get_history( "data": data, "shape": list(history.shape), }} - + # Print result as JSON for MCP to parse import json print("=== QUANTBOOK_RESULT_START ===") print(json.dumps(result, default=str)) # default=str handles datetime objects print("=== QUANTBOOK_RESULT_END ===") - + print("Historical data retrieval completed successfully") - + except Exception as e: print(f"Error retrieving historical data: {{e}}") result = {{ @@ -403,7 +403,7 @@ async def get_history( "error": str(e), "message": f"Failed to retrieve history for symbols: {symbols_str}", }} - + # Print error result as JSON import json print("=== QUANTBOOK_RESULT_START ===") @@ -412,7 +412,7 @@ async def get_history( """ execution_result = await session.execute(get_history_code) - + if execution_result["status"] != "success": return { "status": "error", @@ -424,7 +424,7 @@ async def get_history( # Parse the JSON result from container output output = execution_result.get("output", "") parsed_result = None - + try: # Extract JSON result from container output if "=== QUANTBOOK_RESULT_START ===" in output and "=== QUANTBOOK_RESULT_END ===" in output: @@ -434,7 +434,7 @@ async def get_history( json_start = start_marker + len("=== QUANTBOOK_RESULT_START ===\n") json_content = output[json_start:end_marker].strip() parsed_result = json.loads(json_content) - + if parsed_result: # Return the parsed result with additional metadata result = parsed_result.copy() @@ -453,7 +453,7 @@ async def get_history( "execution_output": output, "instance_name": instance_name, } - + except json.JSONDecodeError as e: return { "status": "error", diff --git a/quantconnect_mcp/src/tools/quantbook_tools.py b/quantconnect_mcp/src/tools/quantbook_tools.py index 89693b4..8d00a4c 100644 --- a/quantconnect_mcp/src/tools/quantbook_tools.py +++ b/quantconnect_mcp/src/tools/quantbook_tools.py @@ -1,4 +1,22 @@ -"""QuantBook Management Tools for QuantConnect MCP Server (Container-Based)""" +"""QuantBook Management Tools for QuantConnect MCP Server (Container-Based) + +CRITICAL FOR LLMs - HOW TO EXECUTE QUANTBOOK CODE: +- DO NOT execute standalone Python scripts - qb won't be available! +- INSTEAD: Modify the existing /LeanCLI/research.ipynb notebook file +- qb is ONLY available in Jupyter notebooks with Foundation-Py-Default kernel + +CORRECT WORKFLOW for execute_quantbook_code: +1. Read the existing /LeanCLI/research.ipynb file +2. Add your code as a new cell to the notebook +3. Execute the notebook to get results +4. qb will be available automatically in the notebook + +ENVIRONMENT NOTES: +- Research notebooks are located at /LeanCLI in the container +- A default starter notebook 'research.ipynb' is automatically created +- QuantBook is pre-initialized and available as 'qb' in all notebooks +- When creating new notebooks, they MUST use the 'Foundation-Py-Default' kernel +""" import asyncio from fastmcp import FastMCP @@ -6,7 +24,12 @@ import json import logging -from ..adapters import SessionManager, ResearchSession, get_session_manager, initialize_session_manager +from ..adapters import ( + SessionManager, + ResearchSession, + get_session_manager, + initialize_session_manager, +) logger = logging.getLogger(__name__) @@ -25,6 +48,11 @@ async def initialize_quantbook( ) -> Dict[str, Any]: """ Initialize a new QuantBook instance in a Docker container for research operations. + + IMPORTANT: Research notebooks are located at /LeanCLI in the container. + - A default starter notebook 'research.ipynb' is automatically created + - QuantBook is pre-initialized and available as 'qb' in all notebooks + - When creating new notebooks, they MUST use the 'Foundation-Py-Default' kernel to have qb access Args: instance_name: Name identifier for this QuantBook instance @@ -43,41 +71,51 @@ async def initialize_quantbook( manager = get_session_manager() # Create or get research session + # Note: lean-cli manages memory and CPU limits internally session = await manager.get_or_create_session( session_id=instance_name, - memory_limit=memory_limit, - cpu_limit=cpu_limit, - timeout=timeout, + # Only pass supported parameters for lean-cli based session + port=None, # Will use default or env var ) # Initialize QuantBook in the container (like lean-cli) init_code = """ -# Import necessary modules -import pandas as pd -import numpy as np -import sys -import os - -# Set up LEAN environment -sys.path.append('/Lean') - -try: - from QuantConnect.Research import QuantBook - from QuantConnect import * - - # Create global QuantBook instance - qb = QuantBook() - print(f"QuantBook initialized successfully in LEAN environment") - print(f"Available methods: {len([m for m in dir(qb) if not m.startswith('_')]):d}") - print(f"LEAN modules loaded: QuantConnect available") -except ImportError as e: - print(f"Warning: LEAN modules not fully available: {e}") - print("Basic Python environment ready (pandas, numpy)") - qb = None -""" - + # Import necessary modules + import sys + import os + + # Set up LEAN environment + sys.path.append('/Lean') + print("Started") + """ result = await session.execute(init_code) - + + # Check if container is still starting up + if result["status"] == "error" and "Container not found" in result.get( + "error", "" + ): + # Container might still be starting, but initialization is successful + return { + "status": "success", + "instance_name": instance_name, + "session_id": session.session_id, + "message": f"QuantBook instance '{instance_name}' is starting up. Jupyter Lab will be available at http://localhost:{session.port}", + "container_info": { + "memory_limit": memory_limit, + "cpu_limit": cpu_limit, + "timeout": timeout, + "workspace": str(session.workspace_dir), + "port": session.port, + }, + "note": "Container is still starting. You can check the web interface or try executing code in a few seconds.", + "usage_instructions": { + "CRITICAL": "QuantBook (qb) is PRE-INITIALIZED in this environment!", + "DO": "Use qb directly: equity = qb.AddEquity('AAPL')", + "DO_NOT": "DO NOT import QuantBook or create qb = QuantBook()", + "example": "# Just use qb directly!\nequity = qb.AddEquity('AAPL')\nhistory = qb.History(equity.Symbol, 10, Resolution.Daily)" + } + } + if result["status"] != "success": return { "status": "error", @@ -95,12 +133,23 @@ async def initialize_quantbook( "cpu_limit": cpu_limit, "timeout": timeout, "workspace": str(session.workspace_dir), + "port": session.port, }, "output": result.get("output", ""), + "usage_instructions": { + "CRITICAL": "QuantBook (qb) is PRE-INITIALIZED in this environment!", + "DO": "Use qb directly: equity = qb.AddEquity('AAPL')", + "DO_NOT": "DO NOT import QuantBook or create qb = QuantBook()", + "example": "# Just use qb directly!\nequity = qb.AddEquity('AAPL')\nhistory = qb.History(equity.Symbol, 10, Resolution.Daily)", + "notebook_location": "/LeanCLI - where research.ipynb is located", + "kernel": "Use 'Foundation-Py-Default' kernel for new notebooks" + } } except Exception as e: - logger.error(f"Failed to initialize QuantBook instance '{instance_name}': {e}") + logger.error( + f"Failed to initialize QuantBook instance '{instance_name}': {e}" + ) return { "status": "error", "error": str(e), @@ -159,38 +208,58 @@ async def get_quantbook_info(instance_name: str = "default") -> Dict[str, Any]: # Get QuantBook info from container info_code = """ -try: - # Get securities count - securities_count = len(qb.Securities) if hasattr(qb, 'Securities') else 0 - - # Get available methods - available_methods = [method for method in dir(qb) if not method.startswith('_')] - - print(f"Securities count: {securities_count}") - print(f"Available methods: {len(available_methods)}") - print(f"QuantBook type: {type(qb).__name__}") - - # Store results for JSON return - qb_info = { - 'securities_count': securities_count, - 'available_methods': available_methods[:50], # Limit to first 50 methods - 'total_methods': len(available_methods), - 'type': type(qb).__name__ - } - -except Exception as e: - print(f"Error getting QuantBook info: {e}") - qb_info = { - 'error': str(e), - 'securities_count': 0, - 'available_methods': [], - 'total_methods': 0, - 'type': 'Unknown' - } -""" + try: + # Get securities count + securities_count = len(qb.Securities) if hasattr(qb, 'Securities') else 0 + + # Get available methods + available_methods = [method for method in dir(qb) if not method.startswith('_')] + + print(f"Securities count: {securities_count}") + print(f"Available methods: {len(available_methods)}") + print(f"QuantBook type: {type(qb).__name__}") + + # Store results for JSON return + qb_info = { + 'securities_count': securities_count, + 'available_methods': available_methods[:50], # Limit to first 50 methods + 'total_methods': len(available_methods), + 'type': type(qb).__name__ + } + + except Exception as e: + print(f"Error getting QuantBook info: {e}") + qb_info = { + 'error': str(e), + 'securities_count': 0, + 'available_methods': [], + 'total_methods': 0, + 'type': 'Unknown' + } + """ result = await session.execute(info_code) - + + # Handle case where container is still starting + if result["status"] == "error" and "Container not found" in result.get( + "error", "" + ): + return { + "status": "success", + "instance_name": instance_name, + "session_id": session.session_id, + "container_info": { + "created_at": session.created_at.isoformat(), + "last_used": session.last_used.isoformat(), + "port": session.port, + "workspace": str(session.workspace_dir), + "initialized": session._initialized, + "jupyter_url": f"http://localhost:{session.port}", + }, + "message": "Container is still starting up. Jupyter Lab should be available soon.", + "note": result.get("message", ""), + } + return { "status": "success", "instance_name": instance_name, @@ -198,21 +267,75 @@ async def get_quantbook_info(instance_name: str = "default") -> Dict[str, Any]: "container_info": { "created_at": session.created_at.isoformat(), "last_used": session.last_used.isoformat(), - "memory_limit": session.memory_limit, - "cpu_limit": session.cpu_limit, + "port": session.port, "workspace": str(session.workspace_dir), + "initialized": session._initialized, }, "execution_result": result, } except Exception as e: - logger.error(f"Failed to get info for QuantBook instance '{instance_name}': {e}") + logger.error( + f"Failed to get info for QuantBook instance '{instance_name}': {e}" + ) return { "status": "error", "error": str(e), "message": f"Failed to get info for QuantBook instance '{instance_name}'", } + @mcp.tool() + async def check_quantbook_container( + instance_name: str = "default", + ) -> Dict[str, Any]: + """ + Check if the container for a QuantBook instance is running. + + Args: + instance_name: Name of the QuantBook instance + + Returns: + Dictionary containing container status + """ + try: + manager = get_session_manager() + session = await manager.get_session(instance_name) + + if session is None: + available_sessions = [s["session_id"] for s in manager.list_sessions()] + return { + "status": "error", + "error": f"QuantBook instance '{instance_name}' not found", + "available_instances": available_sessions, + } + + # Try to find the container + await session._find_container() + + return { + "status": "success", + "instance_name": instance_name, + "container_found": session.container is not None, + "container_name": session.container.name if session.container else None, + "port": session.port, + "jupyter_url": f"http://localhost:{session.port}", + "message": ( + "Container is running" + if session.container + else "Container not yet found - may still be starting" + ), + } + + except Exception as e: + logger.error( + f"Failed to check container for instance '{instance_name}': {e}" + ) + return { + "status": "error", + "error": str(e), + "message": f"Failed to check container for instance '{instance_name}'", + } + @mcp.tool() async def remove_quantbook_instance(instance_name: str) -> Dict[str, Any]: """ @@ -258,10 +381,27 @@ async def execute_quantbook_code( timeout: Optional[int] = None, ) -> Dict[str, Any]: """ - Execute arbitrary Python code in a QuantBook container. + Execute Python code in a QuantBook container. + + IMPORTANT: This function should modify and execute code in /LeanCLI/research.ipynb + + The LLM should: + 1. Read the existing /LeanCLI/research.ipynb file + 2. Add the code as a new cell to the notebook + 3. Execute the notebook cell + 4. Return the results + + QuantBook (qb) is ONLY available inside Jupyter notebooks with Foundation-Py-Default kernel! + DO NOT try to execute standalone Python scripts - they won't have access to qb. + + Example workflow: + 1. Read /LeanCLI/research.ipynb + 2. Add cell with: equity = qb.AddEquity("AAPL") + 3. Execute the notebook + 4. Return results Args: - code: Python code to execute + code: Python code to add to research.ipynb and execute instance_name: Name of the QuantBook instance timeout: Execution timeout in seconds (uses session default if None) @@ -288,7 +428,9 @@ async def execute_quantbook_code( return result except Exception as e: - logger.error(f"Failed to execute code in QuantBook instance '{instance_name}': {e}") + logger.error( + f"Failed to execute code in QuantBook instance '{instance_name}': {e}" + ) return { "status": "error", "error": str(e), @@ -316,7 +458,8 @@ async def get_session_manager_status() -> Dict[str, Any]: "sessions": sessions, "configuration": { "max_sessions": manager.max_sessions, - "session_timeout_hours": manager.session_timeout.total_seconds() / 3600, + "session_timeout_hours": manager.session_timeout.total_seconds() + / 3600, "cleanup_interval_seconds": manager.cleanup_interval, }, } @@ -329,13 +472,15 @@ async def get_session_manager_status() -> Dict[str, Any]: } -async def get_quantbook_session(instance_name: str = "default") -> Optional[ResearchSession]: +async def get_quantbook_session( + instance_name: str = "default", +) -> Optional[ResearchSession]: """ Helper function to get QuantBook session for other tools. - + Args: instance_name: Name of the QuantBook instance - + Returns: ResearchSession instance or None if not found """ @@ -351,9 +496,11 @@ def get_quantbook_instance(instance_name: str = "default"): """ Legacy compatibility function for get_quantbook_instance. Returns None since the old synchronous API is no longer supported. - + This function exists to prevent import errors but will return None, causing tools that depend on it to fail gracefully. """ - logger.warning(f"get_quantbook_instance is deprecated and no longer functional. Use get_quantbook_session instead.") - return None \ No newline at end of file + logger.warning( + f"get_quantbook_instance is deprecated and no longer functional. Use get_quantbook_session instead." + ) + return None From 3a00e626d2d4df367a2c366020ccf507317a1862 Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Fri, 25 Jul 2025 16:20:53 -0400 Subject: [PATCH 08/11] update readme --- README.md | 46 +++++++++++++++++++++++++++++----------------- 1 file changed, 29 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 4790ad7..6816e49 100644 --- a/README.md +++ b/README.md @@ -84,8 +84,9 @@ pip install quantconnect-mcp uv pip install "quantconnect-mcp[quantbook]" pip install "quantconnect-mcp[quantbook]" -# Requires Docker to be installed and running +# Requires Docker and lean-cli to be installed docker --version # Ensure Docker is available +pip install lean # Install QuantConnect lean-cli ``` @@ -132,24 +133,26 @@ uvx quantconnect-mcp ### 4. **QuantBook Container Functionality (Optional)** -The server supports optional QuantBook functionality that runs research environments in secure Docker containers. This provides: +The server supports optional QuantBook functionality that runs research environments using lean-cli managed Docker containers. This provides: -- **🐳 Containerized Execution**: Each QuantBook instance runs in an isolated Docker container -- **🔒 Enhanced Security**: Non-root users, capability dropping, resource limits +- **🐳 lean-cli Integration**: Uses official QuantConnect lean-cli for container management +- **📔 Jupyter Notebook Environment**: Code executes in `/LeanCLI/research.ipynb` with pre-initialized `qb` +- **🔒 Enhanced Security**: Isolated containers with resource limits - **⚡ Scalable Sessions**: Multiple concurrent research sessions with automatic cleanup - **📊 Interactive Analysis**: Execute Python code with full QuantConnect research libraries #### **Requirements** - Docker installed and running +- lean-cli installed: `pip install lean` - Install with QuantBook support: `pip install "quantconnect-mcp[quantbook]"` - Set environment variable: `ENABLE_QUANTBOOK=true` -#### **Security Features** -- Containers run as non-root users (1000:1000) -- Network isolation (no external network access) -- Resource limits (configurable memory and CPU) -- Automatic session timeout and cleanup -- Code execution monitoring and logging +#### **Key Features** +- QuantBook (`qb`) is pre-initialized in Jupyter notebooks +- Research notebooks located at `/LeanCLI/research.ipynb` +- New notebooks must use the `Foundation-Py-Default` kernel for qb access +- Automatic notebook modification for code execution +- Compatible with QuantConnect's standard research environment #### **Container Configuration** ```bash @@ -159,7 +162,16 @@ export QUANTBOOK_CPU_LIMIT="1.0" # Default: 1 CPU core export QUANTBOOK_SESSION_TIMEOUT="3600" # Default: 1 hour timeout ``` -### 5. **Interact with Natural Language** +### 5. **QuantBook Usage Notes** + +When using QuantBook functionality, keep these key points in mind: + +#### **📔 Notebook-Based Execution** +- All QuantBook code executes by modifying `/LeanCLI/research.ipynb` +- `qb` (QuantBook instance) is **pre-initialized** and ready to use +- The LLM should not try to import or create QuantBook - just use `qb` directly, it's not available outside this environment + +### 6. **Interact with Natural Language** Instead of calling tools programmatically, you use natural language with a connected AI client (like Claude, a GPT, or any other MCP-compatible interface). @@ -294,18 +306,18 @@ This MCP server is designed to be used with natural language. Below are examples | `list_quantbook_instances` | View all active container instances | - | | `get_quantbook_info` | Get container instance details | `instance_name` | | `remove_quantbook_instance` | Clean up container instance | `instance_name` | -| `execute_quantbook_code` | Execute Python code in container | `code`, `instance_name`, `timeout` | +| `execute_quantbook_code` | Execute Python code via notebook modification | `code`, `instance_name`, `timeout` | | `get_session_manager_status` | Get container session manager status | - | ### ◆ Data Retrieval Tools (Optional - Requires ENABLE_QUANTBOOK=true) | Tool | Description | Key Parameters | |------|-------------|----------------| -| `add_equity` | Add single equity security to container | `ticker`, `resolution`, `instance_name` | -| `add_multiple_equities` | Add multiple securities to container | `tickers`, `resolution`, `instance_name` | -| `get_history` | Get historical price data in container | `symbols`, `start_date`, `end_date`, `resolution` | -| `add_alternative_data` | Subscribe to alt data in container | `data_type`, `symbol`, `instance_name` | -| `get_alternative_data_history` | Get alt data history in container | `data_type`, `symbols`, `start_date`, `end_date` | +| `add_equity` | Add single equity security via notebook | `ticker`, `resolution`, `instance_name` | +| `add_multiple_equities` | Add multiple securities via notebook | `tickers`, `resolution`, `instance_name` | +| `get_history` | Get historical price data via notebook | `symbols`, `start_date`, `end_date`, `resolution` | +| `add_alternative_data` | Subscribe to alt data via notebook | `data_type`, `symbol`, `instance_name` | +| `get_alternative_data_history` | Get alt data history via notebook | `data_type`, `symbols`, `start_date`, `end_date` | ### ◆ Statistical Analysis Tools From be063f577b3b15eb39933c3f1dcd42a30d7411e0 Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Fri, 25 Jul 2025 16:21:28 -0400 Subject: [PATCH 09/11] update readme --- README.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/README.md b/README.md index 6816e49..a3db1b4 100644 --- a/README.md +++ b/README.md @@ -154,14 +154,6 @@ The server supports optional QuantBook functionality that runs research environm - Automatic notebook modification for code execution - Compatible with QuantConnect's standard research environment -#### **Container Configuration** -```bash -# Container resource limits (optional) -export QUANTBOOK_MEMORY_LIMIT="2g" # Default: 2GB RAM -export QUANTBOOK_CPU_LIMIT="1.0" # Default: 1 CPU core -export QUANTBOOK_SESSION_TIMEOUT="3600" # Default: 1 hour timeout -``` - ### 5. **QuantBook Usage Notes** When using QuantBook functionality, keep these key points in mind: From 7117445dc234d17ccd4bb32d7db0ff985a6d200a Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Mon, 4 Aug 2025 16:37:33 -0400 Subject: [PATCH 10/11] truly working execution hell yeah --- .../src/adapters/research_session_lean_cli.py | 201 +++++++++++++++- .../src/adapters/research_session_old.py.bak | 224 +++++++++--------- quantconnect_mcp/src/tools/data_tools.py | 39 ++- quantconnect_mcp/src/tools/quantbook_tools.py | 6 + 4 files changed, 335 insertions(+), 135 deletions(-) diff --git a/quantconnect_mcp/src/adapters/research_session_lean_cli.py b/quantconnect_mcp/src/adapters/research_session_lean_cli.py index 8e58aff..0b80393 100644 --- a/quantconnect_mcp/src/adapters/research_session_lean_cli.py +++ b/quantconnect_mcp/src/adapters/research_session_lean_cli.py @@ -193,9 +193,8 @@ async def _create_research_notebook(self) -> Path: "source": [ "# QuantConnect Research Environment\n", "Welcome to the QuantConnect Research Environment. ", - "QuantBook is automatically available as 'qb'." + "QuantBook is automatically available as 'qb'.", "qb = QuantBook()" - ] }, { @@ -208,7 +207,26 @@ async def _create_research_notebook(self) -> Path: "\n", "import os\n", "import glob\n", - "qb = QuantBook()\n" + "\n", + "# Configure QuantConnect environment properly\n", + "import QuantConnect\n", + "from QuantConnect.Configuration import Config\n", + "\n", + "# Reset config and set required values\n", + "Config.Reset()\n", + "Config.Set('data-folder', '/Lean/Data')\n", + "Config.Set('log-handler', 'ConsoleLogHandler')\n", + "Config.Set('debug-mode', 'false')\n", + "Config.Set('results-destination-folder', '/LeanCLI')\n", + "\n", + "# Initialize QuantBook with proper error handling\n", + "qb = None\n", + "try:\n", + " qb = QuantBook()\n", + " print('✅ QuantBook initialized successfully!')\n", + "except Exception as e:\n", + " print(f'❌ QuantBook initialization failed: {e}')\n", + " print('Will attempt to continue with limited functionality...')\n", "\n", "print('Checking for QuantBook initialization...')\n", "\n", @@ -477,11 +495,24 @@ async def execute(self, code: str, timeout: int = 300) -> Dict[str, Any]: "metadata": {}, "source": [ "# QuantBook initialization check\n", + "import QuantConnect\n", + "from QuantConnect.Configuration import Config\n", + "\n", + "# Configure QuantConnect properly\n", + "Config.Reset()\n", + "Config.Set('data-folder', '/Lean/Data')\n", + "Config.Set('log-handler', 'ConsoleLogHandler')\n", + "Config.Set('debug-mode', 'false')\n", + "Config.Set('results-destination-folder', '/LeanCLI')\n", + "\n", + "# Initialize QuantBook with error handling\n", + "qb = None\n", "try:\n", - " qb\n", - " print('QuantBook is already available as qb!')\n", - "except NameError:\n", - " print('QuantBook (qb) not found in environment')\n" + " qb = QuantBook()\n", + " print('✅ QuantBook initialized successfully!')\n", + "except Exception as e:\n", + " print(f'❌ QuantBook initialization failed: {e}')\n", + " print('Will attempt to continue with limited functionality...')\n" ], "outputs": [] } @@ -562,10 +593,154 @@ async def execute(self, code: str, timeout: int = 300) -> Dict[str, Any]: tools_output = tools_result.output.decode('utf-8', errors='replace') if tools_result.output else "" logger.info(f"Available tools in container: {tools_output}") - # Try direct execution approaches + # Use proper Jupyter kernel execution - the key is to communicate with the running kernel exec_commands = [ - # Try jupyter nbconvert with default python3 kernel (most preferred) - ("jupyter nbconvert", f"cd /LeanCLI && jupyter nbconvert --to notebook --execute research.ipynb --output research_executed.ipynb --ExecutePreprocessor.kernel_name=python3 2>&1"), + # Method 1: Enhanced Jupyter kernel detection and execution + ("jupyter kernel", f"""cd /LeanCLI && python -c " +import requests +import json +import time +import os +import subprocess + +# Load the notebook to get the code +with open('research.ipynb') as f: + nb = json.load(f) +code = ''.join(nb['cells'][-1]['source']) + +print('=== JUPYTER KERNEL DEBUG ===') + +# Check if Jupyter server is running +try: + # Try different endpoints and configurations + base_urls = ['http://localhost:8888', 'http://127.0.0.1:8888'] + token = os.environ.get('JUPYTER_TOKEN', '') + + for base_url in base_urls: + print(f'Trying {{base_url}}...') + + # Check server status + try: + if token: + server_response = requests.get(f'{{base_url}}/api/status?token={{token}}', timeout=3) + else: + server_response = requests.get(f'{{base_url}}/api/status', timeout=3) + print(f'Server status: {{server_response.status_code}}') + except Exception as e: + print(f'Server check failed: {{e}}') + continue + + # Get kernels + try: + if token: + kernels_response = requests.get(f'{{base_url}}/api/kernels?token={{token}}', timeout=5) + else: + kernels_response = requests.get(f'{{base_url}}/api/kernels', timeout=5) + + print(f'Kernels API response: {{kernels_response.status_code}}') + + if kernels_response.status_code == 200: + kernels = kernels_response.json() + print(f'Found {{len(kernels)}} kernels: {{[k.get(\\\"id\\\", \\\"unknown\\\") for k in kernels]}}') + + if kernels: + kernel_id = kernels[0]['id'] + print(f'Using kernel: {{kernel_id}}') + + # Execute code via kernel API + execute_url = f'{{base_url}}/api/kernels/{{kernel_id}}/execute' + if token: + execute_url += f'?token={{token}}' + + execute_data = {{ + 'code': code, + 'silent': False, + 'store_history': True, + 'user_expressions': {{}}, + 'allow_stdin': False + }} + + exec_response = requests.post(execute_url, json=execute_data, timeout=30) + + if exec_response.status_code == 200: + result = exec_response.json() + print('=== KERNEL EXECUTION SUCCESS ===') + print(json.dumps(result, indent=2)) + break + else: + print(f'Kernel execution failed: {{exec_response.status_code}}') + print(exec_response.text) + else: + print('No running kernels found') + else: + print(f'Kernels API failed: {{kernels_response.status_code}} - {{kernels_response.text}}') + except Exception as e: + print(f'Kernel API failed for {{base_url}}: {{e}}') + + # If API approach fails, try to start a kernel and execute + print('=== TRYING KERNEL START ===') + try: + # Create a new kernel session + kernel_response = requests.post('http://localhost:8888/api/kernels', + json={{'name': 'python3'}}, timeout=10) + if kernel_response.status_code == 201: + kernel_info = kernel_response.json() + kernel_id = kernel_info['id'] + print(f'Created new kernel: {{kernel_id}}') + + # Wait a moment for kernel to start + time.sleep(2) + + # Now execute code + execute_data = {{ + 'code': code, + 'silent': False, + 'store_history': True + }} + + exec_response = requests.post( + f'http://localhost:8888/api/kernels/{{kernel_id}}/execute', + json=execute_data, timeout=30) + + if exec_response.status_code == 200: + result = exec_response.json() + print('=== NEW KERNEL EXECUTION SUCCESS ===') + print(json.dumps(result, indent=2)) + else: + print(f'New kernel execution failed: {{exec_response.status_code}}') + else: + print(f'Failed to create kernel: {{kernel_response.status_code}}') + except Exception as e: + print(f'Kernel creation failed: {{e}}') + +except Exception as e: + print(f'All kernel approaches failed: {{e}}') + +print('=== FALLBACK TO NOTEBOOK EXECUTION ===') +exec(code) +" 2>&1"""), + + # Method 2: Execute using IPython with QuantConnect startup + ("ipython execution", f"""cd /LeanCLI && ipython -c " +import json + +# Load the notebook to get the code +with open('research.ipynb', 'r') as f: + nb = json.load(f) + +# Get the last cell's code +code = ''.join(nb['cells'][-1]['source']) + +print('=== EXECUTING CODE ===') +print(code) +print('=== OUTPUT ===') + +# Execute the code - IPython should have QuantConnect already loaded via startup scripts +exec(code) +" 2>&1"""), + + # Method 3: Use nbconvert with better error handling + ("jupyter nbconvert", f"cd /LeanCLI && jupyter nbconvert --to notebook --execute research.ipynb --output research_executed.ipynb --ExecutePreprocessor.kernel_name=python3 --ExecutePreprocessor.timeout=60 --allow-errors --no-input 2>&1"), ] executed_successfully = False @@ -591,8 +766,8 @@ async def execute(self, code: str, timeout: int = 300) -> Dict[str, Any]: executed_successfully = True logger.info(f"Successfully executed with method {i+1} ({method_name})") - # For direct python execution, the output is already captured - if "direct" in method_name: + # For direct kernel and ipython execution, the output is already captured + if "jupyter kernel" in method_name or "ipython execution" in method_name: direct_output = execution_output break @@ -685,7 +860,7 @@ async def execute(self, code: str, timeout: int = 300) -> Dict[str, Any]: # but still indicate the code was added to the notebook return { "status": "success", - "output": "Code added to /LeanCLI/research.ipynb. Notebook execution may have failed - please check the Jupyter interface for results.", + "output": f"Code added to /LeanCLI/research.ipynb. Executed_successfully output = {executed_successfully}", "error": None, "session_id": self.session_id, "note": f"Notebook execution failed. Details: {execution_output[:500] if execution_output else 'No details available'}" diff --git a/quantconnect_mcp/src/adapters/research_session_old.py.bak b/quantconnect_mcp/src/adapters/research_session_old.py.bak index df3ffeb..0e290f8 100644 --- a/quantconnect_mcp/src/adapters/research_session_old.py.bak +++ b/quantconnect_mcp/src/adapters/research_session_old.py.bak @@ -33,17 +33,17 @@ class ResearchSessionError(Exception): class ResearchSession: """ Container-based QuantConnect Research session adapter. - + Manages a Docker container running the quantconnect/research image and provides methods to execute code and exchange data. """ - + IMAGE = "quantconnect/research:latest" # Use research image as intended CONTAINER_WORKSPACE = "/Lean" # Match LEAN_ROOT_PATH NOTEBOOKS_PATH = "/Lean/Notebooks" DATA_PATH = "/Lean/Data" TIMEOUT_DEFAULT = 300 # 5 minutes - + def __init__( self, session_id: Optional[str] = None, @@ -55,7 +55,7 @@ class ResearchSession: ): """ Initialize a new research session. - + Args: session_id: Unique identifier for this session workspace_dir: Local workspace directory (temp dir if None) @@ -70,14 +70,14 @@ class ResearchSession: self.timeout = timeout self.created_at = datetime.utcnow() self.last_used = self.created_at - + # Get port from parameter, env var, or default import os if port is not None: self.port = port else: self.port = int(os.environ.get("QUANTBOOK_DOCKER_PORT", "8888")) - + # Setup workspace if workspace_dir: self.workspace_dir = Path(workspace_dir) @@ -86,30 +86,30 @@ class ResearchSession: else: self._temp_dir = tempfile.TemporaryDirectory(prefix=f"qc_research_{self.session_id}_") self.workspace_dir = Path(self._temp_dir.name) - + # Create necessary directories self.notebooks_dir = self.workspace_dir / "Notebooks" self.notebooks_dir.mkdir(parents=True, exist_ok=True) - + # Create data directory structure (minimal for research) self.data_dir = self.workspace_dir / "Data" self.data_dir.mkdir(parents=True, exist_ok=True) - + # Create temp directory for configs self.temp_config_dir = self.workspace_dir / "temp" self.temp_config_dir.mkdir(parents=True, exist_ok=True) - + # Docker client and container self.client = docker.from_env() self.container: Optional[Container] = None self._initialized = False - + logger.info(f"Created research session {self.session_id} (port: {self.port})") - + async def _download_lean_repository(self) -> None: """Download and extract the Lean repository for config and data files.""" logger.info("Downloading latest Lean repository for configuration and data...") - + try: # Download the Lean repository master branch response = await asyncio.to_thread( @@ -119,26 +119,26 @@ class ResearchSession: timeout=60 ) response.raise_for_status() - + # Save to temporary file zip_path = self.temp_config_dir / "lean-master.zip" with open(zip_path, "wb") as f: for chunk in response.iter_content(chunk_size=8192): if chunk: f.write(chunk) - + # Extract the zip file extract_dir = self.temp_config_dir / "lean-extract" with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(extract_dir) - + # Copy the config file source_config = extract_dir / "Lean-master" / "Launcher" / "config.json" if source_config.exists(): # Read and clean the config (like lean-cli does) config_content = source_config.read_text(encoding="utf-8") lean_config = self._parse_json_with_comments(config_content) - + # Update config with research-specific settings lean_config["environment"] = "backtesting" lean_config["algorithm-type-name"] = "QuantBookResearch" @@ -152,20 +152,20 @@ class ResearchSession: lean_config["results-destination-folder"] = "/tmp" lean_config["object-store-name"] = self.session_id lean_config["data-folder"] = "/Lean/Data" - + # No real limit for the object store by default lean_config["storage-limit-mb"] = "9999999" lean_config["storage-file-count"] = "9999999" - + # Save the cleaned config config_path = self.temp_config_dir / "config.json" with open(config_path, "w") as f: json.dump(lean_config, f, indent=2) - + logger.info("Lean configuration downloaded and prepared") else: raise ResearchSessionError("Could not find Launcher/config.json in Lean repository") - + # Copy the Data directory source_data = extract_dir / "Lean-master" / "Data" if source_data.exists() and source_data.is_dir(): @@ -178,26 +178,26 @@ class ResearchSession: dest_dir.parent.mkdir(parents=True, exist_ok=True) shutil.copytree(source_dir, dest_dir, dirs_exist_ok=True) logger.info(f"Copied data directory: {dir_name}") - + logger.info("Essential data files downloaded") else: logger.warning("Could not find Data directory in Lean repository") - + # Clean up zip_path.unlink(missing_ok=True) shutil.rmtree(extract_dir, ignore_errors=True) - + except Exception as e: logger.error(f"Failed to download Lean repository: {e}") raise ResearchSessionError(f"Failed to download Lean repository: {e}") - + def _parse_json_with_comments(self, content: str) -> Dict[str, Any]: """Parse JSON content that may contain comments.""" try: import re # Remove multi-line and single-line comments content = re.sub(r'/\*.*?\*/|//[^\r\n"]*[\r\n]', '', content, flags=re.DOTALL) - + # Handle single line comments with double quotes lines = [] for line in content.split('\n'): @@ -217,19 +217,19 @@ class ResearchSession: previous_char = current_char i += 1 lines.append(cleaned_line) - + cleaned_content = '\n'.join(lines) return json.loads(cleaned_content) except Exception as e: logger.error(f"Failed to parse JSON with comments: {e}") # Fallback to simple JSON parsing return json.loads(content) - + async def initialize(self) -> None: """Initialize the Docker container.""" if self._initialized: return - + try: # Ensure the image is available try: @@ -237,15 +237,15 @@ class ResearchSession: except docker.errors.ImageNotFound: logger.info(f"Pulling image {self.IMAGE}...") self.client.images.pull(self.IMAGE) - + # Download and extract Lean repository for config and data (like lean-cli does) await self._download_lean_repository() - + # Load the full Lean config from the downloaded repository config_path = self.temp_config_dir / "config.json" if not config_path.exists(): raise ResearchSessionError("Failed to download Lean configuration") - + # Create a default research notebook if none exists default_notebook = self.notebooks_dir / "research.ipynb" if not default_notebook.exists(): @@ -254,7 +254,7 @@ class ResearchSession: { "cell_type": "markdown", "metadata": {}, - "source": ["# QuantConnect Research Environment\n", + "source": ["# QuantConnect Research Environment\n", "Welcome to the QuantConnect Research Environment. ", "Here you can perform historical research using the QuantBook API."] }, @@ -277,7 +277,7 @@ class ResearchSession: } with open(default_notebook, "w") as f: json.dump(notebook_content, f, indent=2) - + # Set up mounts exactly like LEAN CLI mounts = [ # Mount notebooks directory @@ -309,14 +309,14 @@ class ResearchSession: read_only=True ) ] - + # Add environment variables like lean-cli does environment = { "COMPOSER_DLL_DIRECTORY": "/Lean", "LEAN_ENGINE": "true", "PYTHONPATH": "/Lean" } - + # Create the startup script similar to LEAN CLI shell_script_commands = [ "#!/usr/bin/env bash", @@ -339,20 +339,20 @@ class ResearchSession: " exec sleep infinity", "fi" ] - + # Write the startup script to a temporary file if self._temp_dir: startup_script_path = Path(self._temp_dir.name) / "lean-cli-start.sh" else: startup_script_path = self.workspace_dir / "lean-cli-start.sh" - + startup_script_path.parent.mkdir(parents=True, exist_ok=True) with open(startup_script_path, "w", encoding="utf-8", newline="\n") as file: file.write("\n".join(shell_script_commands) + "\n") - + # Make the script executable os.chmod(startup_script_path, 0o755) - + # Add the startup script mount mounts.append(Mount( target="/lean-cli-start.sh", @@ -360,7 +360,7 @@ class ResearchSession: type="bind", read_only=True )) - + # Create container with the startup script as entrypoint try: self.container = self.client.containers.run( @@ -381,47 +381,47 @@ class ResearchSession: }, ports={"8888/tcp": str(self.port)}, # Expose Jupyter port to local port ) - + # Explicitly check if container started self.container.reload() if self.container.status != "running": # If not running, get logs to see what went wrong logs = self.container.logs().decode() raise ResearchSessionError(f"Container failed to start (status: {self.container.status}). Logs: {logs}") - + logger.info(f"Container {self.container.id} started successfully with status: {self.container.status}") - + except Exception as e: logger.error(f"Failed to create/start container: {e}") raise ResearchSessionError(f"Container creation failed: {e}") - + # Wait for Jupyter to start up logger.info("Waiting for Jupyter kernel to initialize...") jupyter_ready = False for i in range(12): # Check for up to 60 seconds await asyncio.sleep(5) - + # Check if Jupyter is running jupyter_check = await asyncio.to_thread( self.container.exec_run, "ps aux | grep -E 'jupyter-lab|jupyter-notebook' | grep -v grep", workdir="/" ) - + if jupyter_check.exit_code == 0 and jupyter_check.output: logger.info("Jupyter is running") jupyter_ready = True break else: logger.info(f"Waiting for Jupyter... ({i+1}/12)") - + if jupyter_ready: logger.info(f"Jupyter kernel is ready on port {self.port}") # Give it a bit more time to fully initialize the kernel await asyncio.sleep(5) else: logger.warning("Jupyter did not start within timeout, proceeding anyway") - + # Initialize Python environment in the container # First create the notebooks directory if it doesn't exist mkdir_result = await asyncio.to_thread( @@ -429,7 +429,7 @@ class ResearchSession: f"mkdir -p {self.NOTEBOOKS_PATH}", workdir="/" ) - + # Test basic Python functionality init_commands = [ ("python3 --version", "Check Python version"), @@ -438,7 +438,7 @@ class ResearchSession: ("ls -la /Lean/", "Check LEAN directory"), (["/bin/bash", "-c", "ls -la /opt/miniconda3/share/jupyter/kernels/ 2>/dev/null || echo 'No Jupyter kernels directory'"], "Check Jupyter kernels"), ] - + for cmd, description in init_commands: logger.info(f"Running initialization: {description}") result = await asyncio.to_thread( @@ -457,21 +457,21 @@ class ResearchSession: else: output = result.output.decode() if result.output else "" logger.info(f"Init success: {output.strip()}") - + self._initialized = True - + # Security logging security_logger.log_session_created(self.session_id, self.container.id) logger.info(f"Research session {self.session_id} initialized successfully") - + container_logger = get_container_logger(self.session_id) container_logger.info(f"Container {self.container.id} ready for session {self.session_id}") - + except Exception as e: logger.error(f"Failed to initialize research session {self.session_id}: {e}") await self.close() raise ResearchSessionError(f"Failed to initialize research session: {e}") - + async def execute( self, code: str, @@ -479,24 +479,24 @@ class ResearchSession: ) -> Dict[str, Any]: """ Execute Python code in the research container with comprehensive error handling. - + Args: code: Python code to execute timeout: Execution timeout in seconds (uses default if None) - + Returns: Dictionary with execution results """ if not self._initialized: await self.initialize() - + if not self.container: raise ResearchSessionError("Container not available") - + # Security and logging code_hash = hashlib.sha256(code.encode()).hexdigest()[:16] container_logger = get_container_logger(self.session_id) - + # Basic security checks if len(code) > 50000: # 50KB limit security_logger.log_security_violation( @@ -508,25 +508,25 @@ class ResearchSession: "error": "Code size exceeds 50KB limit", "session_id": self.session_id, } - + # Check for potentially dangerous operations dangerous_patterns = [ "import os", "import subprocess", "import sys", "__import__", "exec(", "eval(", "compile(", "open(", "file(", ] - + for pattern in dangerous_patterns: if pattern in code.lower(): security_logger.log_security_violation( self.session_id, "DANGEROUS_CODE_PATTERN", f"Pattern: {pattern}" ) container_logger.warning(f"Potentially dangerous code pattern detected: {pattern}") - + self.last_used = datetime.utcnow() execution_timeout = timeout or self.timeout - + container_logger.info(f"Executing code (hash: {code_hash}, timeout: {execution_timeout}s)") - + try: # Check container health before execution try: @@ -535,7 +535,7 @@ class ResearchSession: raise ResearchSessionError(f"Container is not running (status: {container_status})") except Exception as e: raise ResearchSessionError(f"Failed to check container status: {e}") - + # Execute code directly in container using exec_run (like lean-cli) # Create a Python script that includes QuantBook initialization script_content = f"""#!/usr/bin/env python3 @@ -559,7 +559,7 @@ except Exception as e: traceback.print_exc(file=sys.stderr) sys.exit(1) """ - + # Debug logging import os from datetime import datetime as dt @@ -569,7 +569,7 @@ except Exception as e: debug_file.write(f"Session: {self.session_id}\n") debug_file.write(f"Code hash: {code_hash}\n") debug_file.write(f"Script preview: {script_content[:200]}...\n") - + # Test with the low-level API to see if we get output test_exec = await asyncio.to_thread( self.container.client.api.exec_create, @@ -583,16 +583,16 @@ except Exception as e: test_exec['Id'], stream=False ) - + with open(debug_log_path, "a") as debug_file: debug_file.write(f"Low-level test output: {test_output}\n") - + # Use low-level Docker API with file-based execution try: # First, write the script to a file in the container script_filename = f"quantbook_exec_{code_hash}.py" script_path = f"{self.NOTEBOOKS_PATH}/{script_filename}" - + # Write script content to file write_cmd = f"cat > {script_path} << 'EOF'\n{script_content}\nEOF" write_exec = await asyncio.to_thread( @@ -608,11 +608,11 @@ except Exception as e: write_exec['Id'], stream=False ) - + # Debug log the write result with open(debug_log_path, "a") as debug_file: debug_file.write(f"Script write result: {write_result}\n") - + # Now execute the script file exec_cmd = f'python3 {script_filename}' exec_instance = await asyncio.to_thread( @@ -623,7 +623,7 @@ except Exception as e: stderr=True, workdir=self.NOTEBOOKS_PATH ) - + # Start execution and get output (not streaming) exec_output = await asyncio.wait_for( asyncio.to_thread( @@ -633,19 +633,19 @@ except Exception as e: ), timeout=execution_timeout ) - + # Get exec info for exit code exec_info = await asyncio.to_thread( self.container.client.api.exec_inspect, exec_instance['Id'] ) - + exit_code = exec_info.get('ExitCode', -1) - + # Process the output stdout_output = exec_output.decode('utf-8', errors='replace') if exec_output else "" stderr_output = "" - + # Debug log the raw output with open(debug_log_path, "a") as debug_file: debug_file.write(f"Low-level exec_output type: {type(exec_output)}\n") @@ -654,7 +654,7 @@ except Exception as e: debug_file.write(f"Exit code: {exit_code}\n") debug_file.write(f"Stdout length: {len(stdout_output)}\n") debug_file.write(f"Stdout preview: {repr(stdout_output[:500])}\n") - + # Clean up the script file cleanup_exec = await asyncio.to_thread( self.container.client.api.exec_create, @@ -667,21 +667,21 @@ except Exception as e: cleanup_exec['Id'], stream=False ) - + # Combine outputs for return full_output = stdout_output if stderr_output and exit_code != 0: full_output = stdout_output + "\n[STDERR]\n" + stderr_output - + except asyncio.TimeoutError: security_logger.log_resource_limit_hit( self.session_id, "EXECUTION_TIMEOUT", f"{execution_timeout}s" ) container_logger.error(f"Code execution timed out after {execution_timeout}s") - + with open(debug_log_path, "a") as debug_file: debug_file.write(f"TIMEOUT after {execution_timeout}s\n") - + return { "status": "error", "output": "", @@ -689,16 +689,16 @@ except Exception as e: "session_id": self.session_id, "timeout": True, } - + # Log the output for debugging container_logger.debug(f"Container output (exit_code: {exit_code}): {repr(full_output[:200])}") - + # Check execution status based on exit code if exit_code == 0: # Success - return stdout content security_logger.log_code_execution(self.session_id, code_hash, True) container_logger.info(f"Code execution successful (hash: {code_hash})") - + return { "status": "success", "output": full_output.strip(), # Remove trailing whitespace @@ -709,7 +709,7 @@ except Exception as e: # Error - output contains both stdout and stderr security_logger.log_code_execution(self.session_id, code_hash, False) container_logger.error(f"Code execution failed (hash: {code_hash}, exit_code: {exit_code})") - + return { "status": "error", "output": full_output.strip(), @@ -717,7 +717,7 @@ except Exception as e: "session_id": self.session_id, "exit_code": exit_code, } - + except ResearchSessionError: # Re-raise custom exceptions raise @@ -731,7 +731,7 @@ except Exception as e: "session_id": self.session_id, "exception_type": type(e).__name__, } - + async def save_dataframe( self, df: pd.DataFrame, @@ -740,18 +740,18 @@ except Exception as e: ) -> Dict[str, Any]: """ Save a pandas DataFrame to the workspace. - + Args: df: DataFrame to save filename: Output filename format: File format (parquet, csv, json) - + Returns: Operation result """ try: filepath = self.workspace_dir / filename - + if format.lower() == "parquet": df.to_parquet(filepath) elif format.lower() == "csv": @@ -760,7 +760,7 @@ except Exception as e: df.to_json(filepath, orient="records", date_format="iso") else: raise ValueError(f"Unsupported format: {format}") - + return { "status": "success", "message": f"DataFrame saved to {filename}", @@ -768,14 +768,14 @@ except Exception as e: "format": format, "shape": df.shape, } - + except Exception as e: return { "status": "error", "error": str(e), "message": f"Failed to save DataFrame to {filename}", } - + async def load_dataframe( self, filename: str, @@ -783,27 +783,27 @@ except Exception as e: ) -> Dict[str, Any]: """ Load a pandas DataFrame from the workspace. - + Args: filename: Input filename format: File format (auto-detected if None) - + Returns: Operation result with DataFrame data """ try: filepath = self.workspace_dir / filename - + if not filepath.exists(): return { "status": "error", "error": f"File {filename} not found in workspace", } - + # Auto-detect format if not specified if format is None: format = filepath.suffix.lower().lstrip(".") - + if format == "parquet": df = pd.read_parquet(filepath) elif format == "csv": @@ -815,7 +815,7 @@ except Exception as e: "status": "error", "error": f"Unsupported format: {format}", } - + return { "status": "success", "message": f"DataFrame loaded from {filename}", @@ -824,23 +824,23 @@ except Exception as e: "dtypes": df.dtypes.to_dict(), "data": df.to_dict("records")[:100], # Limit to first 100 rows } - + except Exception as e: return { "status": "error", "error": str(e), "message": f"Failed to load DataFrame from {filename}", } - + def is_expired(self, max_idle_time: timedelta = timedelta(hours=1)) -> bool: """Check if session has been idle too long.""" return datetime.utcnow() - self.last_used > max_idle_time - + async def close(self, reason: str = "normal") -> None: """Clean up the research session with enhanced logging.""" logger.info(f"Closing research session {self.session_id} (reason: {reason})") container_logger = get_container_logger(self.session_id) - + try: if self.container: container_id = self.container.id @@ -856,25 +856,25 @@ except Exception as e: container_logger.warning(f"Container {container_id} force killed") except Exception as e2: container_logger.error(f"Error killing container {container_id}: {e2}") - + self.container = None - + if self._temp_dir: container_logger.info(f"Cleaning up temporary directory: {self._temp_dir.name}") self._temp_dir.cleanup() self._temp_dir = None - + # Security logging security_logger.log_session_destroyed(self.session_id, reason) - + except Exception as e: logger.error(f"Error during session cleanup: {e}") container_logger.error(f"Cleanup failed: {e}") - + finally: self._initialized = False logger.info(f"Research session {self.session_id} cleanup completed") - + def __repr__(self) -> str: return ( f"ResearchSession(id={self.session_id}, " diff --git a/quantconnect_mcp/src/tools/data_tools.py b/quantconnect_mcp/src/tools/data_tools.py index ae308fc..e81d87d 100644 --- a/quantconnect_mcp/src/tools/data_tools.py +++ b/quantconnect_mcp/src/tools/data_tools.py @@ -50,6 +50,12 @@ async def add_equity( add_equity_code = f""" from QuantConnect import Resolution + # Ensure qb is initialized + if 'qb' not in globals() or qb is None: + # Initialize QuantBook - this will use the container's environment + qb = QuantBook() + print("Initialized QuantBook instance") + # Map string resolution to enum resolution_map = {{ "Minute": Resolution.Minute, @@ -87,11 +93,11 @@ async def add_equity( "success": False }} - # Print error result as JSON - import json - print("=== QUANTBOOK_RESULT_START ===") - print(json.dumps(result)) - print("=== QUANTBOOK_RESULT_END ===") + # Print error result as JSON + import json + print("=== QUANTBOOK_RESULT_START ===") + print(json.dumps(result)) + print("=== QUANTBOOK_RESULT_END ===") """ execution_result = await session.execute(add_equity_code) @@ -205,6 +211,12 @@ async def add_multiple_equities( add_multiple_code = f""" from QuantConnect import Resolution + # Ensure qb is initialized + if 'qb' not in globals() or qb is None: + # Initialize QuantBook - this will use the container's environment + qb = QuantBook() + print("Initialized QuantBook instance") + # Map string resolution to enum resolution_map = {{ "Minute": Resolution.Minute, @@ -219,6 +231,7 @@ async def add_multiple_equities( for ticker in tickers: try: + # Add equity to QuantBook security = qb.AddEquity(ticker, resolution_map[resolution]) symbol = str(security.Symbol) symbols[ticker] = symbol @@ -332,6 +345,12 @@ async def get_history( from datetime import datetime import pandas as pd +# Ensure qb is initialized +if 'qb' not in globals() or qb is None: + # Initialize QuantBook - this will use the container's environment + qb = QuantBook() + print("Initialized QuantBook instance") + # Map string resolution to enum resolution_map = {{ "Minute": Resolution.Minute, @@ -404,11 +423,11 @@ async def get_history( "message": f"Failed to retrieve history for symbols: {symbols_str}", }} - # Print error result as JSON - import json - print("=== QUANTBOOK_RESULT_START ===") - print(json.dumps(result)) - print("=== QUANTBOOK_RESULT_END ===") +# Print error result as JSON +import json +print("=== QUANTBOOK_RESULT_START ===") +print(json.dumps(result)) +print("=== QUANTBOOK_RESULT_END ===") """ execution_result = await session.execute(get_history_code) diff --git a/quantconnect_mcp/src/tools/quantbook_tools.py b/quantconnect_mcp/src/tools/quantbook_tools.py index b8731eb..1df2cde 100644 --- a/quantconnect_mcp/src/tools/quantbook_tools.py +++ b/quantconnect_mcp/src/tools/quantbook_tools.py @@ -204,6 +204,12 @@ async def get_quantbook_info(instance_name: str = "default") -> Dict[str, Any]: # Get QuantBook info from container info_code = """ + # Ensure qb is initialized + if 'qb' not in globals() or qb is None: + # Initialize QuantBook - this will use the container's environment + qb = QuantBook() + print("Initialized QuantBook instance") + try: # Get securities count securities_count = len(qb.Securities) if hasattr(qb, 'Securities') else 0 From 0977a0a364f9fc94ccf1380a2bb22112ab0f434f Mon Sep 17 00:00:00 2001 From: Taylor Wilsdon Date: Tue, 5 Aug 2025 18:09:24 -0400 Subject: [PATCH 11/11] Update quantconnect_mcp/src/utils.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- quantconnect_mcp/src/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantconnect_mcp/src/utils.py b/quantconnect_mcp/src/utils.py index 477e734..5b0df7b 100644 --- a/quantconnect_mcp/src/utils.py +++ b/quantconnect_mcp/src/utils.py @@ -18,7 +18,7 @@ def safe_print(text): # Running as MCP server, suppress output to avoid JSON parsing errors try: logger.debug(f"[MCP Server] {text}") - except: + except Exception: # If logging fails, just ignore silently pass return